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@Article{huang_kuo_2010,
  author   = {H. H. Huang and Y. H. Kuo},
  title    = {Cross-Lingual Document Representation and Semantic Similarity Measure: A Fuzzy Set and Rough Set Based Approach},
  journal  = {IEEE Transactions on Fuzzy Systems},
  year     = {2010},
  volume   = {18},
  number   = {6},
  pages    = {1098-1111},
  month    = {Dec},
  issn     = {1063-6706},
  abstract = {As cross-lingual information retrieval is attracting increasing attention, tools that measure cross-lingual semantic similarity between documents are becoming desirable. In this paper, two aspects of cross-lingual semantic document similarity measures are investigated: One is document representation, and the other is the formulation of similarity measures. Fuzzy set and rough set theories are applied to capture the inherently fuzzy relationships among concepts expressed by natural languages. Our approach first develops a language-independent sense-level document representation based on the fuzzy set model to reduce the barrier between different languages and further explores the fuzzy-rough hybrid approach to obtain a more robust macrosense-level document representation through the partitioning of the integrated sense association network of the document collection into macrosenses. Then, Tversky's notion of similarity and the F1 measure on information retrieval are adopted to formulate, respectively, two document similarity measures with fuzzy set operations on the two proposed document representations. The effectiveness of our approach is demonstrated by its success rate in identifying the English translations to their corresponding Chinese documents in a collection of Chinese-English parallel documents. Moreover, the proposed approach can be easily extended to process documents in other languages. It is believed that the proposed representations, along with the similarity measures, will enable more effective text mining processes.},
  doi      = {10.1109/TFUZZ.2010.2065811},
  keywords = {document handling;fuzzy set theory;information retrieval;natural language processing;rough set theory;Chinese document;Chinese-English parallel document;cross-lingual document representation;cross-lingual information retrieval;cross-lingual semantic document similarity measure;fuzzy set model;fuzzy set operation;fuzzy set theory;integrated sense association network;language-independent sense-level document representation;natural language;rough set theory;text mining process;Approximation methods;Computational modeling;Correlation;Fuzzy sets;Hidden Markov models;Information retrieval;Intelligent systems;Internet;Natural languages;Pragmatics;Robustness;Search engines;Semantics;Set theory;Text mining;Web pages;Cross-lingual;document representation;fuzzy–rough hybrid;sense association network;similarity measure},
}

@InProceedings{elhussein_nakata_2010,
  author    = {M. Elhussein and K. Nakata},
  title     = {Cross-Lingual Information Retrieval as a Side Effect of Tagging},
  booktitle = {2010 IEEE Second International Conference on Social Computing},
  year      = {2010},
  pages     = {582-586},
  month     = {Aug},
  abstract  = {Tagging is used for annotating and categorizing documents, and facilitating information retrieval. Users use tags freely to express their views of the provided content. This freedom allows the user to use any language in tagging. This can be considered as an aid for cross-lingual information retrieval (CLIR) since it can help retrieve documents and other contents from different languages other than the query language. This paper investigates the use of Arabic tags in del.icio.us and analyzes the role they play in CLIR.},
  doi       = {10.1109/SocialCom.2010.92},
  keywords  = {document handling;information retrieval;linguistics;natural language processing;query languages;Arabic tags;cross-lingual information retrieval;document annotation;document categorization;query language;Collaboration;Google;Semantics;Stochastic processes;Tagging;Videos;CLIR;del.icio.us;tagging;user collaboration},
}

@InProceedings{zhao_shang_2010,
  author    = {Z. D. Zhao and M. s. Shang},
  title     = {User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop},
  booktitle = {2010 Third International Conference on Knowledge Discovery and Data Mining},
  year      = {2010},
  pages     = {478-481},
  month     = {Jan},
  abstract  = {Collaborative Filtering (CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.},
  doi       = {10.1109/WKDD.2010.54},
  keywords  = {Internet;computational complexity;information filtering;Hadoop;cloud computing platform;computational complexity;recommender systems;user-based collaborative-filtering recommendation algorithms;Cloud computing;Collaboration;Collaborative work;Computer science;Data engineering;Filtering algorithms;Knowledge engineering;Partitioning algorithms;Recommender systems;Scalability;Map-Reduce;cloud computing;collaborative filtering;hadoop;recommender systems},
}

@Conference{liu_etal_2010,
  author        = {Liu, H. and Zeng, D. and Xia, F. and Li, H.Q.},
  title         = {Bilinear models for item recommendation based on tags},
  booktitle     = {Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics},
  year          = {2010},
  pages         = {463-466},
  month         = {July},
  note          = {cited By},
  abstract      = {Recently, collaborative tagging has been gaining increasing popularity in a large variety of websites. The tags generated from collaborative tagging provide highly abstracted information about users' personal taste on information item, and therefore could be used to profile both users and items. However, flattening the three dimensional user-tag-item matrix into two-way matrices will lead to loss of two dimensional relationships between users, items and tags completely. In this paper, we use a predictive bilinear model to capture the informative interaction patterns among user-tag and item-tag matrices, and employ the Nonnegative Matrix Factorization (NMF) algorithm to extract lower dimensional representative features from tags. Experiments on two real-world datasets show that our approach substantially outperforms the traditional CF methods as well as tag-based recommendation methods reported in the literature. © 2010 IEEE.},
  affiliation   = {Institute of Automation, Chinese Academy of Sciences, China; Department of Management Information Systems, University of Arizona, United States},
  art_number    = {5551605},
  document_type = {Conference Paper},
  doi           = {10.1109/SOLI.2010.5551605},
  journal       = {Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010},
  keywords      = {Web sites;matrix decomposition;recommender systems;Websites;collaborative tagging;item recommendation;nonnegative matrix factorization algorithm;predictive bilinear model;Measurement},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957773482&doi=10.1109%2fSOLI.2010.5551605&partnerID=40&md5=62d841aa729dc6847338c8d83a20628e},
}

@InProceedings{chang_wang_2011,
  author    = {H. C. Chang and C. Y. Wang},
  title     = {No Cue, No Clue? Understanding Information Interaction in Social Bookmarking Services},
  booktitle = {2011 Eighth International Conference on Information Technology: New Generations},
  year      = {2011},
  pages     = {867-872},
  month     = {April},
  abstract  = {Social book marking services allow users sharing similar interests to save and exchange web content they find useful or relevant. Book marking is especially important for content not located by the top ranked web pages or easily explored with a traditional search engine. This study investigated the effects that the major context of use (work vs. non-work) has had on experiences with the social book marking service. A within-subject experiment was conducted with 77 subjects. Each took part in both work and non-work information tasks by interacting with six navigation aids. The MANOVA analysis results showed that the contexts of use, work versus non-work, have a significant effect on user information interaction experiences. Moreover, user expertise for work-related tasks has a significant impact on the user perceptions of navigation aids as helpful way finding cues. Understanding how users interact with various way finding cues can enlighten design considerations for refining social navigation functions. For social book marking service providers, the findings of this study offer new insights into user-centered design from an information interaction perspective.},
  doi       = {10.1109/ITNG.2011.150},
  keywords  = {Internet;human computer interaction;search engines;social networking (online);user centred design;MANOVA analysis;Web content exchange;Web pages;information interaction;nonwork information task;search engine;social bookmarking services;social navigation function;user perception;user-centered design;work information task;Collaboration;Context;Human computer interaction;Humans;Information science;Navigation;Tagging;information interaction;social bookmarking;social navigation;social tagging},
}

@InProceedings{ferrer_eerola_2011,
  author    = {R. Ferrer and T. Eerola},
  title     = {Looking Beyond Genres: Identifying Meaningful Semantic Layers from Tags in Online Music Collections},
  booktitle = {2011 10th International Conference on Machine Learning and Applications and Workshops},
  year      = {2011},
  volume    = {2},
  pages     = {112-117},
  month     = {Dec},
  abstract  = {A scheme for identifying the semantic layers of music-related tags is presented. Arguments are provided why the applications of the tags cannot be effectively pursued without a reasonable understanding of their semantic qualities. The identification scheme consists of a set of filters. The first is related with social consensus, user-count ratio, and n-gram properties of tags. The next relies on look-up functions across multiple databases to determine the probable semantic layer of each tag. Examples of the semantic layers with prevalence rates are given based on application of the scheme to a subset of the Million Song Dataset. Finally, a validation of the results was carried out with an independent, smaller hand-annotated dataset, in which high agreement between the identification provided by the scheme and annotations was found.},
  doi       = {10.1109/ICMLA.2011.89},
  keywords  = {information retrieval;music;natural language processing;look-up function;music-related tag;n-gram;online music collection;semantic layer;social consensus;user-count ratio;Databases;Electronic publishing;Encyclopedias;Instruments;Internet;Semantics;music information retrieval;musical genre;semantic layers;social tags},
}

@InProceedings{wu_etal_2012b,
  author    = {Z. Wu and Y. Liu and Y. Wu},
  title     = {Prediction of social tag frequency's power law distribution with RGF model},
  booktitle = {2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  year      = {2012},
  pages     = {1884-1889},
  month     = {Oct},
  abstract  = {The power law distribution of social tag frequency is one of the most important statistic characteristics in social tagging system. Researchers have proposed several models to predict this distribution, but these models depend on specific system. What's more, data fitting methods were failed to describe the raw data precisely compared with a mathematical model. Random Group Formation (RGF) model is a newly proposed mathematical model which could predict the power law distribution precisely. This model does not depend on the specific system. It could predict the power law distribution precisely using only three values: total number of elements, groups and the number of elements in the largest group. In this paper, RGF model is employed to predict the power law distribution of tag frequency. The experiments show that the power law distribution of tag frequency can be predicted precisely by three values, and the distribution is independent on specific system. The analysis of the tag dataset using RGF model shows that the exponent of power law decreases with the increasing of the dataset's size.},
  doi       = {10.1109/ICSMC.2012.6378013},
  issn      = {1062-922X},
  keywords  = {data analysis;social networking (online);statistical distributions;RGF model;data fitting method;dataset size;element number value;group value;power law distribution;random group formation model;social tag frequency;social tagging system;statistical characteristics;tag dataset analysis;Market research;Mathematical model;Predictive models;Tagging;RGF model;power law;social tag},
}

@InProceedings{du_etal_2012,
  author    = {W. H. Du and J. W. Rau and J. W. Huang and Y. S. Chen},
  title     = {Improving the quality of tags using state transition on progressive image search and recommendation system},
  booktitle = {2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  year      = {2012},
  pages     = {3233-3238},
  month     = {Oct},
  abstract  = {In recent years, tagging methods have been widely used on the Web for multimedia search and recommendation systems. Social tags are the keywords annotated by users to the multimedia objects. These tags contain the information which allows multimedia objects to be located and classified. Unfortunately, irrelevant information and noise are frequently included in such tags, especially when we use social tagging systems. Imperfect as they may be, social tags are still a significant source of human-generated contextual knowledge. We present the Waking And Sleeping (WAS) algorithm to overcome the obstacle of noisy tags. WAS refines social tags of each image and assigns each tag into either the waking state or the sleeping state. With WAS, the recommendation system not only enhances the accuracy of the recommendation results based on tags with higher quality but also reduces the consumption of computational resources. The experimental results demonstrate that the proposed approach is superior to compared methods.},
  doi       = {10.1109/ICSMC.2012.6378289},
  issn      = {1062-922X},
  keywords  = {image classification;image retrieval;multimedia computing;social networking (online);WAS algorithm;computational resource consumption;human-generated contextual knowledge;multimedia object classification;multimedia search;noisy tag;progressive image search;recommendation system;sleeping state;social tagging system;state transition;tag quality;tagging method;user annotated keyword;waking and sleeping algorithm;waking state;Accuracy;Databases;Internet;Multimedia communication;Noise measurement;Rabbits;Tagging;Image Annotations;Recommendation System;Tag;Tag Quality Improvement},
}

@InProceedings{mooney_etal_2012,
  author    = {P. Mooney and P. Corcoran and H. Sun and L. Yan},
  title     = {Citizen Generated Spatial Data and Information: Risks and Opportunities},
  booktitle = {2012 International Conference on Industrial Control and Electronics Engineering},
  year      = {2012},
  pages     = {1990-1993},
  month     = {Aug},
  abstract  = {The last few years has seen the use of mobile technology become ubiquituos. Many millions of citizens around the world own smartphones, which they use for both personal and business applications. The majority of smartphones are designed towards Location-based Services (LBS). Consequently these smartphones have on-board GPS devices with the ability of locating the user to an accuracy of a few meters. While at the begining of the growth in popularity of smartphones and LBS technologies most users simply consumed services: where is the nearest coffee shop or subway station? Today, citizens are generating spatial data and information at ever increasing volumes. Examples include: georeferenced photographic collections, location-based social networking such as FourSquare, and volunteered geographic information (VGI). VGI is an exciting movement whereby citizens collect spatial data and information about (their own or another) locality. This content is then shared in collaborative projects such as OpenStreetMap, Geonames, Google Maps Mashups, or WikiMapia. This paper explores the risks involved in using this user-generated spatial data and information with specific emphasis on OpenStreetMap. Most citizens are not specialists in geographic surveying or cartography. Our paper provides results of a large case-study of high edit geographical features. We show that user generated spatial data is a very dynamic but has many inconsistencies.},
  doi       = {10.1109/ICICEE.2012.529},
  keywords  = {Global Positioning System;cartography;geographic information systems;groupware;human computer interaction;mobile computing;social networking (online);user interfaces;FourSquare;Geonames;Google Maps Mashups;LBS;OpenStreetMap;VGI;WikiMapia;cartography;citizen generated spatial data;citizen generated spatial information;collaborative projects;geographic surveying;georeferenced photographic collections;location-based services;location-based social networking;microblogging;mobile technology;onboard GPS devices;smartphones;volunteered geographic information;Communities;Geospatial analysis;Internet;Mobile radio mobility management;Spatial databases;Tagging;Spatial Data;Web 2.0;citizen generated information;data quality},
}

@Article{ivanov_etal_2012,
  author   = {I. Ivanov and P. Vajda and J. S. Lee and T. Ebrahimi},
  title    = {In Tags We Trust: Trust modeling in social tagging of multimedia content},
  journal  = {IEEE Signal Processing Magazine},
  year     = {2012},
  volume   = {29},
  number   = {2},
  pages    = {98-107},
  month    = {March},
  issn     = {1053-5888},
  abstract = {Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.},
  doi      = {10.1109/MSP.2011.942345},
  keywords = {information retrieval;multimedia computing;social networking (online);trusted computing;unsolicited e-mail;multimedia content retrieval;online social networks;social tagging;spam annotations;trust modeling;Content management;Information analysis;Information retrieval;Multimedia communication;Noise measurement;Online services;Search problems;Social network services;Tagging},
}

@Article{sang_etal_2012,
  author   = {J. Sang and C. Xu and J. Liu},
  title    = {User-Aware Image Tag Refinement via Ternary Semantic Analysis},
  journal  = {IEEE Transactions on Multimedia},
  year     = {2012},
  volume   = {14},
  number   = {3},
  pages    = {883-895},
  month    = {June},
  issn     = {1520-9210},
  abstract = {Large-scale user contributed images with tags are easily available on photo sharing websites. However, the noisy or incomplete correspondence between the images and tags prohibits them from being leveraged for precise image retrieval and effective management. To tackle the problem of tag refinement, we propose a method of Ranking based Multi-correlation Tensor Factorization (RMTF), to jointly model the ternary relations among user, image, and tag, and further to precisely reconstruct the user-aware image-tag associations as a result. Since the user interest or background can be explored to eliminate the ambiguity of image tags, the proposed RMTF is believed to be superior to the traditional solutions, which only focus on the binary image-tag relations. During the model estimation, we employ a ranking based optimization scheme to interpret the tagging data, in which the pair-wise qualitative difference between positive and negative examples is used, instead of the point-wise 0/1 confidence. Specifically, the positive examples are directly decided by the observed user-image-tag interrelations, while the negative ones are collected with respect to the most semantically and contextually irrelevant tags. Extensive experiments on a benchmark Flickr dataset demonstrate the effectiveness of the proposed solution for tag refinement. We also show attractive performances on two potential applications as the by-products of the ternary relation analysis.},
  doi      = {10.1109/TMM.2012.2188782},
  keywords = {Web sites;data handling;image retrieval;matrix decomposition;optimisation;semantic Web;tensors;Flickr dataset;RMTF;image retrieval;model estimation;photo sharing Web sites;point-wise 0/1 confidence;ranking based multicorrelation tensor factorization;ranking based optimization scheme;tagging data interpretation;ternary relation analysis;ternary semantic analysis;user-aware image tag refinement;user-aware image-tag associations;user-image-tag interrelations;Correlation;Data models;Media;Noise measurement;Semantics;Tagging;Tensile stress;Factor analysis;social media;tag refinement;tensor factorization},
}

@Article{pammer_etal_2012,
  author          = {Pammer, V. and Kump, B. and Lindstaedt, S.},
  title           = {Tag-based algorithms can predict human ratings of which objects a picture shows},
  journal         = {Multimedia Tools and Applications},
  year            = {2012},
  volume          = {59},
  number          = {2},
  pages           = {441-462},
  note            = {cited By},
  abstract        = {Collaborative tagging platforms allow users to describe resources with freely chosen keywords, so called tags. The meaning of a tag as well as the precise relation between a tag and the tagged resource are left open for interpretation to the user. Although human users mostly have a fair chance at interpreting this relation, machines do not. In this paper we study the characteristics of the problem to identify descriptive tags, i.e. tags that relate to visible objects in a picture. We investigate the feasibility of using a tag-based algorithm, i.e. an algorithm that ignores actual picture content, to tackle the problem. Given the theoretical feasibility of a well-performing tag-based algorithm, which we show via an optimal algorithm, we describe the implementation and evaluation of a WordNet-based algorithm as proof-of-concept. These two investigations lead to the conclusion that even relatively simple and fast tag-based algorithms can yet predict human ratings of which objects a picture shows. Finally, we discuss the inherent difficulty both humans and machines have when deciding whether a tag is descriptive or not. Based on a qualitative analysis, we distinguish between definitional disagreement, difference in knowledge, disambiguation and difference in perception as reasons for disagreement between raters. © Springer Science+Business Media, LLC 2011.},
  affiliation     = {Know-Center, Inffeldgasse 21a, 8010 Graz, Austria; Knowledge Management Institute, Graz University of Technology, Tübingen, Germany},
  author_keywords = {Descriptive tags; Flickr; Image tagging behaviour; Semantic annotation; WordNet},
  document_type   = {Conference Paper},
  doi             = {10.1007/s11042-011-0761-x},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861886947&doi=10.1007%2fs11042-011-0761-x&partnerID=40&md5=076c3eb2a21d3e836568d288a0de64e0},
}

@Article{zhang_etal_2012,
  author    = {Zi{-}Ke Zhang and Tao Zhou and Yi{-}Cheng Zhang},
  title     = {Tag-Aware Recommender Systems: {A} State-of-the-art Survey},
  journal   = {CoRR},
  year      = {2012},
  volume    = {abs/1202.5820},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1202-5820},
  keywords  = {rank1},
  timestamp = {Wed, 07 Jun 2017 14:43:08 +0200},
  url       = {http://arxiv.org/abs/1202.5820},
}

@Article{lue_etal_2012,
  author   = {Linyuan Lü and Matúš Medo and Chi Ho Yeung and Yi-Cheng Zhang and Zi-Ke Zhang and Tao Zhou},
  title    = {Recommender systems},
  journal  = {Physics Reports},
  year     = {2012},
  volume   = {519},
  number   = {1},
  pages    = {1 - 49},
  issn     = {0370-1573},
  note     = {Recommender Systems},
  doi      = {http://dx.doi.org/10.1016/j.physrep.2012.02.006},
  keywords = {Recommender systems, Information filtering, Networks},
  url      = {http://www.sciencedirect.com/science/article/pii/S0370157312000828},
}

@Article{kolda_bader_2009,
  author     = {Kolda, Tamara G. and Bader, Brett W.},
  title      = {Tensor Decompositions and Applications},
  journal    = {SIAM Rev.},
  year       = {2009},
  volume     = {51},
  number     = {3},
  pages      = {455--500},
  month      = aug,
  issn       = {0036-1445},
  acmid      = {1655230},
  address    = {Philadelphia, PA, USA},
  doi        = {10.1137/07070111X},
  issue_date = {August 2009},
  keywords   = {canonical decomposition (CANDECOMP), higher-order principal components analysis (Tucker), higher-order singular value decomposition (HOSVD), multilinear algebra, multiway arrays, parallel factors (PARAFAC), tensor decompositions},
  numpages   = {46},
  publisher  = {Society for Industrial and Applied Mathematics},
  url        = {http://dx.doi.org/10.1137/07070111X},
}

@Article{treude_storey_2012,
  author   = {C. Treude and M. A. Storey},
  title    = {Work Item Tagging: Communicating Concerns in Collaborative Software Development},
  journal  = {IEEE Transactions on Software Engineering},
  year     = {2012},
  volume   = {38},
  number   = {1},
  pages    = {19-34},
  month    = {Jan},
  issn     = {0098-5589},
  abstract = {In collaborative software development projects, work items are used as a mechanism to coordinate tasks and track shared development work. In this paper, we explore how “tagging,” a lightweight social computing mechanism, is used to communicate matters of concern in the management of development tasks. We present the results from two empirical studies over 36 and 12 months, respectively, on how tagging has been adopted and what role it plays in the development processes of several professional development projects with more than 1,000 developers in total. Our research shows that the tagging mechanism was eagerly adopted by the teams, and that it has become a significant part of many informal processes. Different kinds of tags are used by various stakeholders to categorize and organize work items. The tags are used to support finding of tasks, articulation work, and information exchange. Implicit and explicit mechanisms have evolved to manage the tag vocabulary. Our findings indicate that lightweight informal tool support, prevalent in the social computing domain, may play an important role in improving team-based software development practices.},
  doi      = {10.1109/TSE.2010.91},
  keywords = {groupware;information retrieval;software development management;collaborative software development project;lightweight informal tool support;social computing mechanism;tag vocabulary;team-based software development practice;work item tagging;Collaboration;Data mining;Programming;Software engineering;Tagging;Tagging;articulation work;collaboration;software development;task management;work items., rank2},
}

@Article{kaminskas_ricci_2012,
  author   = {Marius Kaminskas and Francesco Ricci},
  title    = {Contextual music information retrieval and recommendation: State of the art and challenges},
  journal  = {Computer Science Review},
  year     = {2012},
  volume   = {6},
  number   = {2},
  pages    = {89 - 119},
  issn     = {1574-0137},
  doi      = {http://dx.doi.org/10.1016/j.cosrev.2012.04.002},
  keywords = {Music information retrieval, Music recommender systems, Context-aware services, Affective computing, Social computing, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S1574013712000135},
}

@Book{carminati_etal_2013,
  title     = {Security and Trust in Online Social Networks},
  publisher = {Morgan \& Claypool},
  year      = {2013},
  author    = {Barbara Carminati and Elena Ferrari and Marco Viviani},
  isbn      = {9781627052665},
  abstract  = {The enormous success and diffusion that online social networks (OSNs) are encountering nowadays is vastly apparent. Users' social interactions now occur using online social media as communication channels; personal information and activities are easily exchanged both for recreational and business purposes in order to obtain social or economic advantages. In this scenario, OSNs are considered critical applications with respect to the security of users and their resources, for their characteristics alone: the large amount of personal information they manage, big economic upturn connected to their commercial use, strict interconnection among users and resources characterizing them, as well as user attitude to easily share private data and activities with strangers. In this book, we discuss three main research topics connected to security in online social networks: (i) trust management, because trust can be intended as a measure of the perception of security (in terms of risks/benefits) hat users in an OSN have with respect to other (unknown/little-known) parties; (ii) controlled information sharing, because in OSNs, where personal information is not only connected to user profiles, but spans across users' social activities and interactions, users must be provided with the possibility to directly control information flows; and (iii) identity management, because OSNs are subjected more and more to malicious attacks that, with respect to traditional ones, have the advantage of being more effective by leveraging the social network as a new medium for reaching victims. For each of these research topics, in this book we provide both theoretical concepts as well as an overview of the main solutions that commercial/non-commercial actors have proposed over the years. We also discuss some of the most promising research directions in these fields.},
  booktitle = {Security and Trust in Online Social Networks},
  doi       = {10.2200/S00549ED1V01Y201311SPT008},
  pages     = {120-},
  url       = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6813068},
}

@InProceedings{zhu_etal_2013,
  author    = {T. Zhu and G. Li and Y. Ren and W. Zhou and P. Xiong},
  title     = {Privacy Preserving for Tagging Recommender Systems},
  booktitle = {2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)},
  year      = {2013},
  volume    = {1},
  pages     = {81-88},
  month     = {Nov},
  abstract  = {Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called afolksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, adversaries may re-identify a user and her/his sensitivity information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Lap lace noise to the weight of tags We present extensive experimental results on two real world datasets, Delicious and Bibsonomy. While the personalization algorithmis successful in both cases.},
  doi       = {10.1109/WI-IAT.2013.12},
  keywords  = {Internet;data privacy;pattern clustering;random processes;recommender systems;Bibsonomy;De.licio.us;Internet users;Laplace noise;background information;differential privacy;folksonomy;personalization algorithm;personalized tags;privacy-preserving operations;private releasing algorithm;private tag clustering;private tag selection;randomized domain;replacement tags;resource annotation;sensitivity information;tagging datasets;tagging recommender systems;user identity preservation;user profile;weight perturbation;Clustering algorithms;Data privacy;Noise;Privacy;Recommender systems;Sensitivity;Tagging;Differential Privacy;Privacy Preserving;Tagging Recommender System},
}

@InProceedings{6498238,
  author    = {R. M. Feitosa and S. Labidi and A. L. S. dos Santos and N. Santos},
  title     = {Social Recommendation in Location-Based Social Network Using Text Mining},
  booktitle = {2013 4th International Conference on Intelligent Systems, Modelling and Simulation},
  year      = {2013},
  pages     = {67-72},
  month     = {Jan},
  abstract  = {This paper intends to create an application that performs the Data Mining with textual information linked to geolocation data. The structure of the information is distributed in heterogeneous and complex scenario that presents a Social Network. The purpose is that on the final extraction of information, the results are worked for Social Recommendation. However, the recommender systems present some failures in the filtering of the results and the way they are suggested to users. Then, the article presents a methodology of social recommendation to Location-Based Social Network with text mining techniques, and expose issues that still need to research more effective and consolidated results.},
  doi       = {10.1109/ISMS.2013.99},
  issn      = {2166-0662},
  keywords  = {data mining;information filtering;mobile computing;recommender systems;social networking (online);text analysis;complex information distribution;data mining;geolocation data;heterogeneous information distribution;information extraction;information filtering;location-based social network;recommender systems;social recommendation;text mining techniques;textual information;Collaboration;Data mining;Filtration;Frequency measurement;Geology;Recommender systems;Social network services;data mining; social recommendation; tags},
}

@Article{rafailidis_daras_2013,
  author   = {D. Rafailidis and P. Daras},
  title    = {The TFC Model: Tensor Factorization and Tag Clustering for Item Recommendation in Social Tagging Systems},
  journal  = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  year     = {2013},
  volume   = {43},
  number   = {3},
  pages    = {673-688},
  month    = {May},
  issn     = {2168-2216},
  abstract = {In this paper, a novel Tensor Factorization and tag Clustering (TFC) model is presented for item recommendation in social tagging systems. The TFC model consists of three distinctive steps, in each of which important innovative elements are proposed. More specifically, through its first step, the content information is exploited to propagate tags between conceptual similar items based on a relevance feedback mechanism, in order to solve sparsity and “cold start” problems. Through its second step, sparsity is further handled, by generating tag clusters and revealing topics, following an innovative tf ·idf weighting scheme. Furthermore, we experimentally prove that a few number of expert tags can improve the performance of quality recommendations, since they contribute to more coherent tag clusters. Through its third step, the latent associations among users, topics, and items are revealed by exploiting the TF technique of high order singular value decomposition. This way the proposed TFC model tackles problems of real-world applications, which produce noise and decrease the quality of recommendations. In our experiments with real-world social data, we show that the proposed TFC model outperforms other state-of-the-art methods, which also exploit the TF technique of HOSVD.},
  doi      = {10.1109/TSMCA.2012.2208186},
  keywords = {pattern clustering;singular value decomposition;social networking (online);tensors;TFC model;content information;innovative elements;item recommendation;quality recommendations;relevance feedback mechanism;singular value decomposition;social tagging systems;tensor factorization and tag clustering;Clustering algorithms;Collaboration;Image color analysis;Recommender systems;Tagging;Tensile stress;Vectors;Content based information retrieval;expert tagging;recommender systems;relevance feedback;social tagging},
}

@InProceedings{lee_etal_2013,
  author    = {I. Lee and G. Cai and K. Lee},
  title     = {Mining Points-of-Interest Association Rules from Geo-tagged Photos},
  booktitle = {2013 46th Hawaii International Conference on System Sciences},
  year      = {2013},
  pages     = {1580-1588},
  month     = {Jan},
  abstract  = {The advent of photo-sharing services results in massive user-generated geo-tagged photos. These photos implicitly and explicitly indicate points-of-interest and their associations. This study aims to combine two data mining techniques: clustering and association rules mining to mine areas of attraction, and their associative patterns. We analyze photos from Flickr in the area of Queensland, Australia, a popular tourist destination hosting the Great Barrier Reef and tropical rain forest. We report interesting experimental results and discuss findings.},
  doi       = {10.1109/HICSS.2013.401},
  issn      = {1530-1605},
  keywords  = {data mining;pattern clustering;social networking (online);Australia;Flickr;Great Barrier Reef;Queensland;associative patterns;attraction area mining;clustering;data mining techniques;photo-sharing services;points-of-interest association rules mining;tropical rain forest;user-generated geo-tagged photos;Association rules;Australia;Business;Educational institutions;Itemsets;Noise;Flickr mining;Point of interest mining;geo-tagged photos},
}

@InProceedings{tang_etal_2013b,
  author    = {X. Tang and Y. Xu and S. Geva},
  title     = {Integrating Time Forgetting Mechanisms into Topic-Based User Interest Profiling},
  booktitle = {2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)},
  year      = {2013},
  volume    = {3},
  pages     = {1-4},
  month     = {Nov},
  abstract  = {The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.},
  doi       = {10.1109/WI-IAT.2013.132},
  keywords  = {data mining;recommender systems;user interfaces;World Wide Web;information overload problem;personalization techniques;temporal effects;time decay mechanisms;time forgetting mechanisms;topic interest measurements;topic-based user interest profiling;user interests mining;user needs;user personalized interests;user profiling techniques;user representations;Accuracy;Collaboration;Equations;Probability;Recommender systems;Tagging;recommender systems;temporal dynamics;time forgetting;user profiling},
}

@InProceedings{misaghian_etal_2013,
  author    = {N. Misaghian and M. Jalali and M. H. Moattar},
  title     = {Resource recommender system based on tag and time for social tagging system},
  booktitle = {ICCKE 2013},
  year      = {2013},
  pages     = {97-101},
  month     = {Oct},
  abstract  = {Recently social tagging systems are increasingly becoming popular. These systems allow users to manage, organize and search their required resources. Most of the available modules in recommender systems, don't consider information such as time that could be effective on the users preferences of a resource. So, one of the existing challenges in resource recommendation systems is the appropriate integration of tag and time information to discover user's taste considering time drift and improving accuracy. In this paper data from social tagging systems are modeled with four-mode tensors to capture four-way correlations between users, items, tags and time information. Then by applying multi-way analysis, latent correlations among users preferences based on time information are revealed, which help to improve the quality of recommendations. The advantages of proposed system are that it considers simultaneous relations among elements, discovers latent associations and improves the quality of recommendations accuracy. The experimental results on a real-world dataset, Citeulike, indicate significant improvements in recommendation quality and better performance compared with the existing systems.},
  doi       = {10.1109/ICCKE.2013.6682832},
  keywords  = {recommender systems;social networking (online);tensors;Citeulike dataset;four-mode tensors;multiway analysis;resource recommender system;social tagging system;time drift;time information;user preferences;Accuracy;Chaos;Context;Lead;Tagging;Tensile stress;Social tagging system;collaborative recommendation system;tensor},
}

@InProceedings{moghaddam_etal_2013,
  author    = {M. G. Moghaddam and A. Mustapha and N. Mustapha and N. M. Sharef},
  title     = {A temporal-focused trustworthiness to enhance trust-based recommender systems},
  booktitle = {2013 13th International Conference on Intellient Systems Design and Applications},
  year      = {2013},
  pages     = {219-223},
  month     = {Dec},
  abstract  = {Collaborative Filtering (CF) is the most successful technology for recommender systems. The technology does not rely on actual content of the items, but instead requires users to indicate preferences, most commonly in the form of ratings. While CF is known for its traditional problems such as cold-start, sparsity and modest accuracy, a trust-based CF has been previously proposed to solve such issues by focusing on trust values among the users. Nonetheless, all existing trust-based approaches use trust as a factor independent from scope, whether explicit or implicit. We argue that trustworthiness should not be the same across all conditions; hence the trust values should change to suit certain scope or focused area. To validate the proposed temporal-focused trustworthiness in this paper, we propose a novel pheromone-based approach to calculate trustworthiness by focusing on time factor. Implementation of the proposed approach is hoped to reduce cold-start and sparsity as well as improve accuracy of the recommendation results.},
  doi       = {10.1109/ISDA.2013.6920738},
  issn      = {2164-7143},
  keywords  = {collaborative filtering;recommender systems;trusted computing;cold-start;collaborative filtering;pheromone-based approach;temporal-focused trustworthiness;time factor;trust values;trust-based CF;trust-based recommender systems;Expert systems;Recommender systems;collaborative filtering;social tagging systems;temporal-focused;trust},
}

@Article{bobadilla_etal_2013,
  author   = {J. Bobadilla and F. Ortega and A. Hernando and A. Gutiérrez},
  title    = {Recommender systems survey},
  journal  = {Knowledge-Based Systems},
  year     = {2013},
  volume   = {46},
  pages    = {109 - 132},
  issn     = {0950-7051},
  doi      = {http://dx.doi.org/10.1016/j.knosys.2013.03.012},
  keywords = {Recommender systems, Collaborative filtering, Similarity measures, Evaluation metrics, Prediction, Recommendation, Hybrid, Social, Internet of things, Cold-start},
  url      = {http://www.sciencedirect.com/science/article/pii/S0950705113001044},
}

@Article{liu_etal_2013b,
  author   = {Jing Liu and Yifan Zhang and Zechao Li and Hanqing Lu},
  title    = {Correlation consistency constrained probabilistic matrix factorization for social tag refinement},
  journal  = {Neurocomputing},
  year     = {2013},
  volume   = {119},
  pages    = {3 - 9},
  issn     = {0925-2312},
  note     = {Intelligent Processing Techniques for Semantic-based Image and Video Retrieval},
  doi      = {http://dx.doi.org/10.1016/j.neucom.2012.02.052},
  keywords = {Matrix factorization, Correlation consistency, Tag refinement, Social image},
  url      = {http://www.sciencedirect.com/science/article/pii/S0925231213000192},
}

@Article{jung_2013,
  author   = {Jason J. Jung},
  title    = {Cross-lingual query expansion in multilingual folksonomies: A case study on Flickr},
  journal  = {Knowledge-Based Systems},
  year     = {2013},
  volume   = {42},
  pages    = {60 - 67},
  issn     = {0950-7051},
  doi      = {http://dx.doi.org/10.1016/j.knosys.2013.01.016},
  keywords = {Social searching, Collective intelligence, Multilingual tagging, Multilingual folksonomy, Lingual practice},
  url      = {http://www.sciencedirect.com/science/article/pii/S0950705113000282},
}

@Article{gan_jiang_2013,
  author   = {Mingxin Gan and Rui Jiang},
  title    = {Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities},
  journal  = {Decision Support Systems},
  year     = {2013},
  volume   = {55},
  number   = {3},
  pages    = {811 - 821},
  issn     = {0167-9236},
  doi      = {http://dx.doi.org/10.1016/j.dss.2013.03.006},
  keywords = {Recommender systems, Collaborative filtering, Power law adjustment, Accuracy, Diversity},
  url      = {http://www.sciencedirect.com/science/article/pii/S0167923613000882},
}

@Article{spiranec_ivanjko_2013,
  author   = {Sonja Špiranec and Tomislav Ivanjko},
  title    = {Experts vs. Novices Tagging Behavior: An Exploratory Analysis},
  journal  = {Procedia - Social and Behavioral Sciences},
  year     = {2013},
  volume   = {73},
  pages    = {456 - 459},
  issn     = {1877-0428},
  note     = {Proceedings of the 2nd International Conference on Integrated Information (IC-ININFO 2012), Budapest, Hungary, August 30 – September 3, 2012},
  doi      = {http://dx.doi.org/10.1016/j.sbspro.2013.02.076},
  keywords = {folksonomies, tagging, tag efficiency, tagging behavior},
  url      = {http://www.sciencedirect.com/science/article/pii/S1877042813003698},
}

@InProceedings{rognon_etal_2013,
  author    = {T. Rognon and R. Strautman and L. Jett and N. Bigdely-Shamlo and S. Makeig and T. Johnson and K. Robbins},
  title     = {CTAGGER: Semi-structured community tagging for annotation and data-mining in event-rich contexts},
  booktitle = {2013 IEEE Global Conference on Signal and Information Processing},
  year      = {2013},
  pages     = {5-8},
  month     = {Dec},
  abstract  = {Analysis of dynamic brain imaging data from EEG, MEG or fMRI requires a common temporal context to enable meta-analysis and data mining across experiments. However, there is no standardized method of annotating events, even from laboratory experiments in controlled settings, and the event-rich environments of real-world brain imaging present a still greater annotation challenge. We have developed a MATLAB toolbox called CTAGGER to enable a user-friendly, semi-structured and expandable strategy for event annotation in dynamic brain imaging and other time-series. To facilitate common labeling and comparison across data collections and laboratories, an individual's annotation can be collected in a common community database to encourage annotation reuse. The tools allow the creation of multiple event overlays to facilitate the reuse and combination of brain imaging data for multiple analyses.},
  doi       = {10.1109/GlobalSIP.2013.6736797},
  keywords  = {biomedical MRI;brain;data acquisition;data analysis;data mining;electroencephalography;human computer interaction;information retrieval;magnetoencephalography;medical computing;meta data;social networking (online);time series;CTAGGER;EEG;MEG;Matlab toolbox;data annotation;data collection;data mining;dynamic brain imaging data analysis;event annotation reusability;expandable strategy;fMRI;meta analysis;semi-structured community tagging;time series;user friendly strategy;Electroencephalography;Laboratories;MATLAB;Roads;Shape;Vehicles;Visualization},
}

@Article{alves_santanche_2013a,
  author   = {Hugo Alves and André Santanchè},
  title    = {Folksonomized ontology and the 3E steps technique to support ontology evolvement},
  journal  = {Web Semantics: Science, Services and Agents on the World Wide Web},
  year     = {2013},
  volume   = {18},
  number   = {1},
  pages    = {19 - 30},
  issn     = {1570-8268},
  note     = {Special Section on the Semantic and Social Web},
  doi      = {http://dx.doi.org/10.1016/j.websem.2012.07.004},
  keywords = {Ontology, Folksonomy, Semantic web, Folksonomized ontology},
  url      = {http://www.sciencedirect.com/science/article/pii/S1570826812000789},
}

@Conference{kailer_etal_2013,
  author          = {Kailer, D. and Mandl, P. and Schill, A.},
  title           = {Rated tags: Adding rating capability to collaborative tagging},
  booktitle       = {2013 International Conference on Cloud and Green Computing},
  year            = {2013},
  pages           = {249-255},
  month           = {Sept},
  note            = {cited By},
  abstract        = {Collaborative tagging is a popular way to organize content. But sometimes it is also used as a way to express opinions, which is normally done through rating-or text review systems. This paper will demonstrate that there is a gap between rating- and text review systems, that limits the ability of users to find the desired item. A novel concept based on collaborative tagging is presented, which is designed to bridge this gap. This concept, named Rated Tags, uses a hybrid approach to combine tagging-, rating- and review functionality. A key element of Rated Tags is the combination of a traditional user generated tag and a 5-star rating scale. Such a tag is called rated tag. We will discuss the design and the challenges of Rated Tags in detail and demonstrate its application based on a prototype. Furthermore we will show that similar related works suffer from an ambiguity problem, which was avoided in Rated Tags. © 2013 IEEE.},
  affiliation     = {Munich University of Applied Sciences, Department of Computer Science and Mathematics, Munich, Germany; TU Dresden, Department of Computer Networks, Dresden, Germany},
  art_number      = {6686038},
  author_keywords = {Collaborative tagging; Rated tags; Ratings; Reviews; Social tagging; Tags},
  document_type   = {Conference Paper},
  doi             = {10.1109/CGC.2013.46},
  journal         = {Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013},
  keywords        = {collaborative filtering;information retrieval;text analysis;5-star rating scale;collaborative filtering;collaborative tagging;rated tags;rating functionality;rating review systems;review functionality;text review systems;Bridges;Cameras;Collaboration;Decision making;Headphones;Prototypes;Tagging;collaborative tagging;rated tags;ratings;reviews;social tagging;tags},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893300880&doi=10.1109%2fCGC.2013.46&partnerID=40&md5=b6d46ec4461cf9033b7e640f6770bf2d},
}

@Article{uddin_etal_2013,
  author          = {Uddin, M.N. and Duong, T.H. and Nguyen, N.T. and Qi, X.-M. and Jo, G.S.},
  title           = {Semantic similarity measures for enhancing information retrieval in folksonomies},
  journal         = {Expert Systems with Applications},
  year            = {2013},
  volume          = {40},
  number          = {5},
  pages           = {1645-1653},
  issn            = {0957-4174},
  note            = {cited By},
  abstract        = {Collaborative tagging systems, also known as folksonomies, enable a user to annotate various web resources with a free set of tags for sharing and searching purposes. Tags in a folksonomy reflect users' collaborative cognition about information. Tags play an important role in a folksonomy as a means of indexing information to facilitate search and navigation of resources. However, the semantics of the tags, and therefore the semantics of the resources, are neither known nor explicitly stated. It is therefore difficult for users to find related resources due to the absence of a consistent semantic meaning among tags. The shortage of relevant tags increases data sparseness and decreases the rate of information extraction with respect to user queries. Defining semantic relationships between tags, resources, and users is an important research issue for the retrieval of related information from folksonomies. In this research, a method for finding semantic relationships among tags is proposed. The present study considers not only the pairwise relationships between tags, resources, and users, but also the relationships among all three. Experimental results using real datasets from Flickr and Del.icio.us show that the method proposed here is more effective than previous methods such as LCH, JCN, and LIN in finding semantic relationships among tags in a folksonomy. © 2012 Elsevier Ltd. All rights reserved.},
  affiliation     = {School of Computer and Information Engineering, Inha University, South Korea; Institute of Informatics, Wroclaw University of Technology, Poland},
  author_keywords = {Collaborative tagging; Folksonomies; Information retrieval; Tag; WordNet},
  document_type   = {Article},
  doi             = {10.1016/j.eswa.2012.09.006},
  keywords        = {Folksonomies, Tag, WordNet, Information retrieval, Collaborative tagging},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872026953&doi=10.1016%2fj.eswa.2012.09.006&partnerID=40&md5=072779fe68162ffeca6711eb851cb292},
}

@InProceedings{amarai_etal_2014,
  author    = {A. Roque do Amarai and L. A. M. Zaina and J. F. Rodrigues and R. de Aguiar and M. C. Barboza},
  title     = {The use of social tagging to support the cataloguing of learning objects},
  booktitle = {2014 IEEE Frontiers in Education Conference (FIE) Proceedings},
  year      = {2014},
  pages     = {1-7},
  month     = {Oct},
  abstract  = {Social networks have become the main media for information dissemination in the so-called Web 2.0. The core of these networks is social tagging, the act of annotating what users see in their social space. In the education domain, social tagging is potentially a useful resource to improve the organization (cataloguing) of large repositories of learning objects. To the present moment, however, many questions are open about social tagging in e-learning. In this work, hence, we proceed to answer three questions: (1) Can social tagging successfully catalog e-learning objects? (2) How do students behave according to Körner's classification: categorizers or describers? and (3) Does social tagging converge to a well-defined descriptive vocabulary of tags? We performed a large experiment with 336 technician students that marked 218 electronic learning objects for about 4,985 times. Our results show that social tagging is a promising practice for e-learning; however some issues must be addressed to prevent an excessive number of categorizer students and, also, a premature convergence of the vocabulary of tags. Our conclusions are specific for the setting of our experiment, but we generalize them as much as possible suggesting guidelines of how to use social tagging in e-learning.},
  doi       = {10.1109/FIE.2014.7044487},
  issn      = {0190-5848},
  keywords  = {Internet;cataloguing;computer aided instruction;information dissemination;pattern classification;social networking (online);Körner classification;Web 2.0;descriptive vocabulary;e-learning;education domain;information dissemination;learning object;organization cataloguing;social network;social tagging;Catalogs;Computer science;Educational institutions;Electronic learning;Search problems;Tagging;Vocabulary;folksonomy;learning objects;social tagging},
}

@InProceedings{manca_etal_2014,
  author    = {M. Manca and L. Boratto and S. Carta},
  title     = {Design and architecture of a friend recommender system in the social bookmarking domain},
  booktitle = {2014 Science and Information Conference},
  year      = {2014},
  pages     = {838-842},
  month     = {Aug},
  abstract  = {Social media systems are becoming more and more popular nowadays. In order to face the overload in the amount of users and content available in these systems, social recommender systems have been developed and are largely studied in the literature. A form of social media, known as social bookmarking system, allows to share bookmarks in a social network. A user adds as a friend or follows another user and receives updates on the bookmarks added by that user. However, no approach in the literature proposes friend recommender systems in the social bookmarking domain. In this paper, we present an analysis of the state-of-the-art on user recommendation in social environments and of the structure of a social bookmarking system, in order to derive a design and an architecture of a friend recommender system in the social bookmarking domain. This study can be useful for any future research in this area, by highlighting the aspects that characterize this domain and the features that this type of recommender system has to offer.},
  doi       = {10.1109/SAI.2014.6918282},
  keywords  = {recommender systems;social networking (online);friend recommender system architecture;friend recommender system design;social bookmarking domain;social environments;social media systems;social network;social recommender systems;user recommendation;Computer architecture;Design methodology;Media;Recommender systems;Tagging;Twitter;Friend Recommendation;Social Bookmarking;Tagging System},
}

@Article{lee_etal_2014,
  author   = {Baozhen Lee and Weiguo Fan and Anna C. Squicciarini and Shilun Ge and Yun Huang},
  title    = {The relativity of privacy preservation based on social tagging},
  journal  = {Information Sciences},
  year     = {2014},
  volume   = {288},
  pages    = {87 - 107},
  issn     = {0020-0255},
  doi      = {http://dx.doi.org/10.1016/j.ins.2014.08.015},
  keywords = {Privacy preservation, Social tagging, Relativity, Hierarchical community},
  url      = {http://www.sciencedirect.com/science/article/pii/S0020025514008044},
}

@Article{wagner_etal_2014,
  author   = {C. Wagner and P. Singer and M. Strohmaier and B. Huberman},
  title    = {Semantic Stability and Implicit Consensus in Social Tagging Streams},
  journal  = {IEEE Transactions on Computational Social Systems},
  year     = {2014},
  volume   = {1},
  number   = {1},
  pages    = {108-120},
  month    = {March},
  issn     = {2329-924X},
  abstract = {One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, images, users, or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources in social tagging systems may become semantically stable over time, as more and more users tag them and implicitly agree on the relative importance of tags for a resource. At the same time, previous work has raised an array of new questions such as: 1) how can we assess semantic stability in a robust and methodical way? 2) does the semantic stabilization varies across different social tagging systems and ultimately, and 3) what are the factors that can explain semantic stabilization in such systems? In this work, we tackle these questions by: 1) presenting a novel and robust method, which overcomes a number of limitations in existing methods; 2) empirically investigating semantic stabilization in different social tagging systems with distinct domains and properties; and 3) detecting potential causes of stabilization and implicit consensus, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams that are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams that are generated via imitation dynamics or natural language phenomena alone.},
  doi      = {10.1109/TCSS.2014.2307455},
  keywords = {information retrieval;natural language processing;centralized vocabulary;imitation dynamics;implicit consensus;natural language phenomena;semantic stability;semantic stabilization;social tagging systems;Natural languages;Robustness;Semantics;Stability analysis;Tagging;Twitter;Vocabulary;Distributional semantics;emergent semantics;social semantics;social tagging;stabilization process},
}

@InProceedings{tian_etal_2014,
  author    = {Y. Tian and D. Lo and J. Lawall},
  title     = {Automated construction of a software-specific word similarity database},
  booktitle = {2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE)},
  year      = {2014},
  pages     = {44-53},
  month     = {Feb},
  abstract  = {Many automated software engineering approaches, including code search, bug report categorization, and duplicate bug report detection, measure similarities between two documents by analyzing natural language contents. Often different words are used to express the same meaning and thus measuring similarities using exact matching of words is insufficient. To solve this problem, past studies have shown the need to measure the similarities between pairs of words. To meet this need, the natural language processing community has built WordNet which is a manually constructed lexical database that records semantic relations among words and can be used to measure how similar two words are. However, WordNet is a general purpose resource, and often does not contain software-specific words. Also, the meanings of words in WordNet are often different than when they are used in software engineering context. Thus, there is a need for a software-specific WordNet-like resource that can measure similarities of words. In this work, we propose an automated approach that builds a software-specific WordNet like resource, named WordSimSEDB, by leveraging the textual contents of posts in StackOverflow. Our approach measures the similarity of words by computing the similarities of the weighted co-occurrences of these words with three types of words in the textual corpus. We have evaluated our approach on a set of software-specific words and compared our approach with an existing WordNet-based technique (WordNetres) to return top-k most similar words. Human judges are used to evaluate the effectiveness of the two techniques. We find that WordNetres returns no result for 55 % of the queries. For the remaining queries, WordNetres returns significantly poorer results.},
  doi       = {10.1109/CSMR-WCRE.2014.6747213},
  keywords  = {database management systems;natural language processing;program debugging;software engineering;StackOverflow;WordNet;WordSimSEDB;automated construction;automated software engineering;bug report categorization;code search;duplicate bug report detection;lexical database;natural language contents;natural language processing;software-specific word similarity database;Java;Measurement;Semantics;Software;Software engineering;Tuning;Vectors, rank1},
}

@Article{du_etal_2014,
  author   = {Helen S. Du and Samuel K.W. Chu and Gary E. Gorman and Felix L.C. Siu},
  title    = {Academic social bookmarking: An empirical analysis of Connotea users},
  journal  = {Library \& Information Science Research},
  year     = {2014},
  volume   = {36},
  number   = {1},
  pages    = {49 - 58},
  issn     = {0740-8188},
  doi      = {http://dx.doi.org/10.1016/j.lisr.2013.06.003},
  keywords = {rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S0740818814000036},
}

@Article{chen_etal_2014b,
  author          = {Chen, Z. and Gao, Q. and Yang, Y.},
  title           = {Cultural difference on the usage pattern of tagging system for knowledge sharing},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2014},
  volume          = {8528 LNCS},
  pages           = {534-545},
  note            = {cited By},
  abstract        = {Collaborative tagging system in online knowledge sharing system benefits the knowledge management of users. It also inspires us a quantitative way how to investigate the effect of culture on knowledge management as tags reflects the usage pattern of users. In this study, the usage pattern of tag in three types of knowledge management websites (i.e. photo, bookmark and book sharing websites) present the influence of Chinese culture upon knowledge management. A set of indices is used to describe the usage pattern of tags and the results indicate that Chinese users behave differently in all three types of knowledge sharing system. © 2014 Springer International Publishing.},
  affiliation     = {Institute of Human Factors and Ergonomics, Industrial Engineering, Tsinghua University, China},
  author_keywords = {Chinese culture; knowledge sharing; tag},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-319-07308-8_51},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903603782&doi=10.1007%2f978-3-319-07308-8_51&partnerID=40&md5=40600e69ab0b3a7ebb4d3e982ceb3b2c},
}

@Conference{lorince_etal_2014,
  author          = {Lorince, J. and Zorowitz, S. and Murdock, J. and Todd, P.M.},
  title           = {Supertagger behavior in building folksonomies},
  year            = {2014},
  pages           = {129-138},
  note            = {cited By},
  abstract        = {A folksonomy is ostensibly an information structure built up by the "wisdom of the crowds", but is the "crowd" really doing the work? Tagging is in fact a sharply skewed process in which a small minority of users generate an overwhelming majority of the annotations. Using data from the social music site Last.fm as a case study, this paper explores the implications of this tagging imbalance. Partitioning the folksonomy into two halves - one created by the prolific minority and the other by the non-prolific majority of taggers - we examine the large-scale differences in these two sub-folksonomies and the users generating them, and then explore several possible accounts of what might be driving these differences. We find that prolific taggers preferentially annotate content in the long-tail of less popular items, use tags with higher information content, and show greater tagging expertise. These results indicate that "supertaggers" not only tag more than their counterparts, but in quantifi- ably different ways. Copyright © 2014 ACM.},
  affiliation     = {Cognitive Science, Indiana University, United States; Psychological and Brain Sciences, Indiana University, United States; Informatics John Hopkins University, United States},
  author_keywords = {Collaborative tagging; Folksonomy; Supertaggers},
  document_type   = {Conference Paper},
  doi             = {10.1145/2615569.2615686},
  journal         = {WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904489299&doi=10.1145%2f2615569.2615686&partnerID=40&md5=939db123f609a67ed4137dd8d3ca3fd1},
}

@Article{santos-neto_etal_2014,
  author        = {Santos-Neto, E. and Condon, D. and Andrade, N. and Iamnitchi, A. and Ripeanu, M.},
  title         = {Reuse, temporal dynamics, interest sharing, and collaboration in social tagging system},
  journal       = {First Monday},
  year          = {2014},
  volume        = {19},
  number        = {7},
  note          = {cited By},
  abstract      = {User-generated content shapes the dynamics of the World Wide Web. In particular, collaborative tagging represents a simple, yet powerful, feature that enables users to share and collaboratively annotate content such as photos and URLs. This collaborative behavior and the pool of user-generated metadata create opportunities to improve existing systems and to design new mechanisms. However, to realize this potential, it is necessary to first understand the usage characteristics of current systems. This work addresses this issue by characterizing three tagging systems (CiteULike, Connotea and del.icio.us) while focusing on three aspects: i) the patterns of information (tags and items) production; ii) the temporal dynamics of users' tag vocabularies; and, iii) the social aspects of tagging systems. The analysis of the patterns of information production shows that users publish new content more often than they annotate already existing content in the system. The opposite, however, occurs for tags; the level of tag reuse is much higher. The study of the temporal dynamics of user vocabularies shows that the growth rate of tag vocabularies across the user population over time decreases at early ages, stabilizes, and returns to increase for older users. Moreover, a closer look into the change of vocabulary contents over time shows that despite the fact that tag vocabularies are slowly growing in size with user age, the relative frequency in which each tag is used converges relatively quickly in a users lifetime. Finally, the characterization of social aspects of tagging unveils the relationship between the implicit user ties, as inferred from the similarity between users' activity, and their explicit social ties, as represented by co-membership in discussion groups or semantic similarity between tag vocabularies. © First Monday, 1995-2014.},
  affiliation   = {Electrical and Computer Engineering Department, University of British Columbia, Canada; University of South Florida, United States; Systems and Computing Department, Universidade Federal de Campina Grande, Brazil},
  document_type = {Article},
  doi           = {10.5210/fm.v19i7.4994},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906057489&doi=10.5210%2ffm.v19i7.4994&partnerID=40&md5=0dfd833d70fe6eb7dfcf8b7a482ede17},
}

@Article{parra-arnau_etal_2014,
  author          = {J. Parra-Arnau and A. Perego and E. Ferrari and J. Forné and D. Rebollo-Monedero},
  title           = {Privacy-Preserving Enhanced Collaborative Tagging},
  journal         = {IEEE Transactions on Knowledge and Data Engineering},
  year            = {2014},
  volume          = {26},
  number          = {1},
  pages           = {180-193},
  month           = {Jan},
  issn            = {1041-4347},
  note            = {cited By},
  abstract        = {Collaborative tagging is one of the most popular services available online, and it allows end user to loosely classify either online or offline resources based on their feedback, expressed in the form of free-text labels (i.e., tags). Although tags may not be per se sensitive information, the wide use of collaborative tagging services increases the risk of cross referencing, thereby seriously compromising user privacy. In this paper, we make a first contribution toward the development of a privacy-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy. Moreover, we analyze how our approach can affect the effectiveness of a policy-based collaborative tagging system that supports enhanced web access functionalities, like content filtering and discovery, based on preferences specified by end users.},
  affiliation     = {Department of Telematics Engineering, Universitat Politècnica de Catalunya, Campus Nord, C./ Jordi Girona 1-3, 08034 Barcelona, Spain; European Commission - Joint Research Centre of the European Commission, Institute for Environment and Sustainability, Unit H06 - Digital Earth and Reference Data, Via E. Fermi, 2749, TP 262, 21027 Ispra VA, Italy; Department of Theoretical and Applied Science, University of Insubria, Via Mazzini 5, I-21100 Varese, Italy},
  art_number      = {6399467},
  author_keywords = {Policy-based collaborative tagging; Privacy-enhancing technology; Privacy-utility tradeoff; Shannon's entropy; Social bookmarking; Tag suppression},
  document_type   = {Article},
  doi             = {10.1109/TKDE.2012.248},
  keywords        = {data privacy;social networking (online);Web access functionalities;collaborative tagging services;content discovery;content filtering;cross referencing;end-user privacy protection;free-text labels;offline resources;online resources;policy-based collaborative tagging system;privacy-enhancing technology;privacy-preserving collaborative tagging service;social bookmarking;tag suppression;Collaboration;Data privacy;Entropy;Privacy;Semantics;Tag clouds;Policy-based collaborative tagging;Shannon's entropy;privacy-enhancing technology;privacy-utility tradeoff;social bookmarking;tag suppression},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890419962&doi=10.1109%2fTKDE.2012.248&partnerID=40&md5=10d4503770c0b996ed99128e65b0e4ff},
}

@Article{carvalho_etal_2015,
  author   = {Carlos Rosemberg Maia de Carvalho and Elizabeth Sucupira Furtado and Vasco Furtado},
  title    = {Does content categorization lead to knowledge building? An experiment in a social bookmarking service},
  journal  = {Computers in Human Behavior},
  year     = {2015},
  volume   = {51},
  pages    = {1177 - 1184},
  issn     = {0747-5632},
  note     = {Computing for Human Learning, Behaviour and Collaboration in the Social and Mobile Networks Era},
  doi      = {http://dx.doi.org/10.1016/j.chb.2015.01.033},
  keywords = {User studies, Social tagging, Collaborative filtering, Collaborative learning, Empirical studies in HCI, Social reputation systems},
  url      = {http://www.sciencedirect.com/science/article/pii/S0747563215000473},
}

@Article{lorince_etal_2015,
  author          = {Lorince, J. and Joseph, K. and Todd, P.M.},
  title           = {Analysis of music tagging and listening patterns: Do tags really function as retrieval aids?},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2015},
  volume          = {9021},
  pages           = {141-152},
  note            = {cited By},
  abstract        = {In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do not support the assumption that tags generally serve as retrieval aids. © Springer International Publishing Switzerland 2015.},
  affiliation     = {Cognitive Science Program, Indiana University, Bloomington, IN, United States; Computation, Organization, and Society Program, Carnegie Mellon University, Pittsburgh, PA, United States},
  author_keywords = {Collaborative tagging; Folksonomy; Memory cues; Music listening; Personal information management; Retrieval aids},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-319-16268-3_15},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925321873&doi=10.1007%2f978-3-319-16268-3_15&partnerID=40&md5=f8564aec2ec258ec3851af8068213779},
}

@Conference{klyachko_2015,
  author          = {Klyachko, E.},
  title           = {Using folksonomy data for determining semantic similarity},
  year            = {2015},
  volume          = {2},
  number          = {14},
  pages           = {119-132},
  note            = {cited By},
  abstract        = {This paper presents a method for measuring semantic similarity. Semantic similarity measures are important for various semantics-oriented natural language processing tasks, such as Textual Entailment or Word Sense Disambiguation. In the paper, a folksonomy graph is used to determine the relatedness of two words. The construction of a folksonomy from a collaborative photo tagging resource is described. The problems which occur during the process are analyzed and solutions are proposed. The structure of the folksonomy is also analyzed. It turns out to be a social network graph. Graph features, such as the path length, or the Jaccard similarity coefficient, are the input parameters for a machine learning classifying algorithm. The comparative importance of the parameters is evaluated. Finally, the method was evaluated in the RUSSE evaluation campaign. The results are lower than most results for distribution-based vector models. However, the model itself is cheaper to build. The failures of the models are analyzed and possible improvements are suggested.},
  affiliation     = {Moscow, Russian Federation},
  author_keywords = {Collaborative tagging; Folksonomy; Semantic similarity; Social networks},
  document_type   = {Conference Paper},
  journal         = {Komp'juternaja Lingvistika i Intellektual'nye Tehnologii},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952760170&partnerID=40&md5=01cfd5eb9ff3e0c2f59dd68ebc8831f2},
}

@Article{ley_seitlinger_2015,
  author          = {Ley, T. and Seitlinger, P.},
  title           = {Dynamics of human categorization in a collaborative tagging system: How social processes of semantic stabilization shape individual sensemaking},
  journal         = {Computers in Human Behavior},
  year            = {2015},
  volume          = {51},
  number          = {PA},
  pages           = {140-151},
  issn            = {0747-5632},
  note            = {cited By},
  abstract        = {We study how categories form and develop over time in a sensemaking task by groups of students employing a collaborative tagging system. In line with distributed cognition theories, we look at both the tags students use and their strength of representation in memory. We hypothesize that categories get more differentiated over time as students learn, and that semantic stabilization on the group level (i.e. the convergence in the use of tags) mediates this relationship. Results of a field experiment that tested the impact of topic study duration on the specificity of tags confirms these hypotheses, although it was not study duration that produced this effect, but rather the effectiveness of the collaborative taxonomy the groups built. In the groups with higher levels of semantic stabilization, we found use of more specific tags and better representation in memory. We discuss these findings with regard to the important role of the information value of tags that would drive both the convergence on the group level as well as a shift to more specific levels of categorization. We also discuss the implication for cognitive science research by highlighting the importance of collaboratively built artefacts in the process of how knowledge is acquired, and implications for educational applications of collaborative tagging environments. © 2015 Published by Elsevier Ltd.},
  affiliation     = {Tallinn University, Tallinn, Estonia; Graz University of Technology, Graz, Austria},
  author_keywords = {Categorization; Collaborative tagging; Distributed cognition; Sensemaking; Social web},
  document_type   = {Article},
  doi             = {10.1016/j.chb.2015.04.053},
  keywords        = {Categorization, Sensemaking, Collaborative tagging, Distributed cognition, Social web, rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929573561&doi=10.1016%2fj.chb.2015.04.053&partnerID=40&md5=97bda05ffce2f794517345161ca07248},
}

@Article{cattuto_etal_2007,
  author    = {Ciro Cattuto and Andrea Baldassarri and Vito Domenico Pietro Servedio and Vittorio Loreto},
  title     = {Vocabulary growth in collaborative tagging systems},
  journal   = {CoRR},
  year      = {2007},
  volume    = {abs/0704.3316},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-0704-3316},
  timestamp = {Wed, 07 Jun 2017 14:40:28 +0200},
  url       = {http://arxiv.org/abs/0704.3316},
}

@InProceedings{jin_nakayama_2016,
  author    = {Jiren Jin and H. Nakayama},
  title     = {Annotation order matters: Recurrent Image Annotator for arbitrary length image tagging},
  booktitle = {2016 23rd International Conference on Pattern Recognition (ICPR)},
  year      = {2016},
  pages     = {2452-2457},
  month     = {Dec},
  abstract  = {Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing different methods, it is not the natural way that humans annotate images. The number of annotated tags should depend on image contents. Inspired by the recent progress in machine translation and image captioning, we propose a novel Recurrent Image Annotator (RIA) model that forms image annotation task as a sequence generation problem so that RIA can natively predict the proper length of tags according to image contents. We evaluate the proposed model on various image annotation datasets. In addition to comparing our model with existing methods using the conventional top-k evaluation measures, we also provide our model as a high quality baseline for the arbitrary length image tagging task. Moreover, the results of our experiments show that the order of tags in training phase has a great impact on the final annotation performance.},
  doi       = {10.1109/ICPR.2016.7900004},
  keywords  = {image annotation;image retrieval;language translation;RIA;annotation accuracy improvement;annotation order matters;arbitrary length image tagging task;automatic image annotation;human annotate images;image annotation datasets;image annotation task;image captioning;image contents;large-scale image management;large-scale image retrieval;learning image-tag correlation;machine translation;recurrent image annotator;sequence generation problem;Correlation;Feature extraction;Indexes;Predictive models;Recurrent neural networks;Training},
}

@Article{goy_etal_2016,
  author   = {Anna Goy and Diego Magro and Giovanna Petrone and Claudia Picardi and Marino Segnan},
  title    = {Ontology-driven collaborative annotation in shared workspaces},
  journal  = {Future Generation Computer Systems},
  year     = {2016},
  volume   = {54},
  pages    = {435 - 449},
  issn     = {0167-739X},
  doi      = {http://dx.doi.org/10.1016/j.future.2015.04.013},
  keywords = {Online collaboration, Collaborative semantic annotation, Ontologies, Knowledge representation, Knowledge sharing, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S0167739X15001053},
}

@Article{hajibayova_jacob_2016,
  author   = {Lala Hajibayova and Elin K. Jacob},
  title    = {An investigation of the levels of abstraction of tags across three resource genres},
  journal  = {Information Processing \& Management},
  year     = {2016},
  volume   = {52},
  number   = {6},
  pages    = {1178 - 1187},
  issn     = {0306-4573},
  doi      = {http://dx.doi.org/10.1016/j.ipm.2016.05.005},
  keywords = {Genre, User-generated vocabulary, Tagging, Level of abstraction of tags, Basic level categories},
  url      = {http://www.sciencedirect.com/science/article/pii/S0306457316301364},
}

@Article{nakatsuji_etal_2016,
  author   = {Makoto Nakatsuji and Hiroyuki Toda and Hiroshi Sawada and Jin Guang Zheng and James A. Hendler},
  title    = {Semantic sensitive tensor factorization},
  journal  = {Artificial Intelligence},
  year     = {2016},
  volume   = {230},
  pages    = {224 - 245},
  issn     = {0004-3702},
  doi      = {http://dx.doi.org/10.1016/j.artint.2015.09.001},
  keywords = {Tensor factorization, Linked open data, Recommendation systems, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S000437021500137X},
}

@Article{li_etal_2016b,
  author   = {Yibai Li and Xuequn Wang and Xiaolin Lin and Mohammad Hajli},
  title    = {Seeking and sharing health information on social media: A net valence model and cross-cultural comparison},
  journal  = {Technological Forecasting and Social Change},
  year     = {2016},
  issn     = {0040-1625},
  doi      = {http://dx.doi.org/10.1016/j.techfore.2016.07.021},
  keywords = {Health information, Social network sites, Social media, Net valence model},
  url      = {http://www.sciencedirect.com/science/article/pii/S0040162516301780},
}

@Article{zhang_etal_2016,
  author   = {L. Zhang and Z. Cai and X. Wang},
  title    = {FakeMask: A Novel Privacy Preserving Approach for Smartphones},
  journal  = {IEEE Transactions on Network and Service Management},
  year     = {2016},
  volume   = {13},
  number   = {2},
  pages    = {335-348},
  month    = {June},
  issn     = {1932-4537},
  abstract = {Users can enjoy personalized services provided by various context-aware applications that collect users' contexts through sensor-equipped smartphones. Meanwhile, serious privacy concerns arise due to the lack of privacy preservation mechanisms. Currently, most mechanisms apply passive defense policies in which the released contexts from a privacy preservation system are always real, leading to a great probability with which an adversary infers the hidden sensitive contexts about the users. In this paper, we apply a deception policy for privacy preservation and present a novel technique, FakeMask, in which fake contexts may be released to provably preserve users' privacy. The output sequence of contexts by FakeMask can be accessed by the untrusted context-aware applications or be used to answer queries from those applications. Since the output contexts may be different from the original contexts, an adversary has greater difficulty in inferring the real contexts. Therefore, FakeMask limits what adversaries can learn from the output sequence of contexts about the user being in sensitive contexts, even if the adversaries are powerful enough to have the knowledge about the system and the temporal correlations among the contexts. The essence of FakeMask is a privacy checking algorithm which decides whether to release a fake context for the current context of the user. We present a novel privacy checking algorithm and an efficient one to accelerate the privacy checking process. Extensive evaluation experiments on real smartphone context traces of users demonstrate the improved performance of FakeMask over other approaches.},
  doi      = {10.1109/TNSM.2016.2559448},
  keywords = {data privacy;mobile computing;security of data;smart phones;FakeMask;context-aware application;privacy checking algorithm;smart phone;user privacy preservation mechanism;Context;Context modeling;Context-aware services;Correlation;Markov processes;Privacy;Smart phones;Privacy protection;privacy protection;semi-Markov model;service},
}

@Book{lorince_todd_2016,
  title         = {Music tagging and listening: Testing the memory cue hypothesis in a collaborative tagging system},
  year          = {2016},
  author        = {Lorince, J. and Todd, P.M.},
  note          = {cited By},
  abstract      = {As an example of exploring human memory cue use in an ecologically valid context, we present ongoing work to examine the “memory cue hypothesis” in collaborative tagging. In collaborative tagging systems, which allow users to assign freeform textual labels to digital resources, it is generally assumed that tags function as memory cues that facilitate future retrieval of the resources to which they are assigned. There is, however, little empirical evidence demonstrating that this is in fact the case. Employing large-scale music listening and tagging data from the social music website Last.fm as a case study, we present a set of time series and information theoretic analytic methods we are using to explore how patterns of content tagging and interaction support or refute the hypothesis that tags function as retreival cues. Early results are, on average, consistent with the hypothesis. There is an immediate practical application of this work to those working with collaborative tagging systems (are user motivations what we think they are?), but our work also comprises contributions of interest to the cognitive science community: First, we are expanding our understanding of how people generate and use memory cues “in the wild.” Second, we are enriching the “toolbox” available to cognitive scientists for studying cognition using large-scale, ecologically valid data that is latent in the logged activity of web users. © 2017 Taylor & Francis.},
  affiliation   = {Northwestern University, United States; Indiana University, United States},
  document_type = {Book Chapter},
  doi           = {10.4324/9781315413570},
  journal       = {Big Data in Cognitive Science},
  pages         = {117-143},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020807801&doi=10.4324%2f9781315413570&partnerID=40&md5=b6811228604393de883531d1de31e9e6},
}

@Article{wang_etal_2014,
  author    = {Peng Wang and Baowen Xu and Yurong Wu and Xiaoyu Zhou},
  title     = {Link Prediction in Social Networks: the State-of-the-Art},
  journal   = {CoRR},
  year      = {2014},
  volume    = {abs/1411.5118},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/WangXWZ14},
  keywords  = {rank1},
  timestamp = {Wed, 07 Jun 2017 14:41:12 +0200},
  url       = {http://arxiv.org/abs/1411.5118},
}

@InProceedings{borrego_etal_2017,
  author    = {G. Borrego and A. L. Morán and R. Palacio},
  title     = {Preliminary Evaluation of a Tag-Based Knowledge Condensation Tool in Agile and Distributed Teams},
  booktitle = {2017 IEEE 12th International Conference on Global Software Engineering (ICGSE)},
  year      = {2017},
  pages     = {51-55},
  month     = {May},
  abstract  = {An important challenge in Agile Global Software Development (AGSD) is architectural knowledge vaporization, i.e., the loss of technical knowledge due to a lack of documentation. In a previous work we identified that technical knowledge is usually available in unstructured textual electronic means (e.g. chat, mail, blogs, etc. - also known as UTEMs) used by AGSD workers during development, although not necessarily easily accessible. Also, we proposed the concept of Knowledge Condensation as a means to recover knowledge from UTEMs, by introducing a structure to their contents along with means to retrieve knowledge. In this work we present our first step towards a Knowledge Condensation tool, through a prototype tagging mechanism to structure architectural knowledge during UTEM interactions. We evaluated the prototype and obtained the following results: (1) a high perception of usefulness and a high projected intention of use, and (2) the identification of 6 tagging behavior profiles from participants. These tagging behaviors can be grouped as (i) participants who tag as expected, (ii) participants who tag more than expected, and (ii) participants who tag less than expected. These results suggest that it is important to cope with "little or no tagging" and "excessive tagging" behaviors to implement knowledge condensation based on UTEM tagging. Furthermore, we identified that tagging could be a good option to structure UTEM interactions, and to retrieve architectural knowledge from UTEM logs. Future work includes improving the tagging mechanism prototype based on the participants' suggestions and overall evaluation results, and implementing a searcher prototype to further evaluate the concept of knowledge condensation based on tagging of UTEM interactions.},
  doi       = {10.1109/ICGSE.2017.14},
  keywords  = {software prototyping;text analysis;AGSD;UTEM interactions;UTEM logs;UTEM tagging;agile global software development;agile-and-distributed teams;architectural knowledge retrieval;architectural knowledge vaporization;tag-based knowledge condensation tool;tagging behavior profiles;tagging mechanism prototype;unstructured textual electronic means;Companies;Documentation;Prototypes;Tagging;Tools;Usability;agile global software development;architectural knowledge;knowledge condensing;knowledge vaporization;unstructured and textual electronic media},
}

@Article{moutafis_etal_2017,
  author   = {P. Moutafis and M. Leng and I. A. Kakadiaris},
  title    = {An Overview and Empirical Comparison of Distance Metric Learning Methods},
  journal  = {IEEE Transactions on Cybernetics},
  year     = {2017},
  volume   = {47},
  number   = {3},
  pages    = {612-625},
  month    = {March},
  issn     = {2168-2267},
  abstract = {In this paper, we first offer an overview of advances in the field of distance metric learning. Then, we empirically compare selected methods using a common experimental protocol. The number of distance metric learning algorithms proposed keeps growing due to their effectiveness and wide application. However, existing surveys are either outdated or they focus only on a few methods. As a result, there is an increasing need to summarize the obtained knowledge in a concise, yet informative manner. Moreover, existing surveys do not conduct comprehensive experimental comparisons. On the other hand, individual distance metric learning papers compare the performance of the proposed approach with only a few related methods and under different settings. This highlights the need for an experimental evaluation using a common and challenging protocol. To this end, we conduct face verification experiments, as this task poses significant challenges due to varying conditions during data acquisition. In addition, face verification is a natural application for distance metric learning because the encountered challenge is to define a distance function that: 1) accurately expresses the notion of similarity for verification; 2) is robust to noisy data; 3) generalizes well to unseen subjects; and 4) scales well with the dimensionality and number of training samples. In particular, we utilize well-tested features to assess the performance of selected methods following the experimental protocol of the state-of-the-art database labeled faces in the wild. A summary of the results is presented along with a discussion of the insights obtained and lessons learned by employing the corresponding algorithms.},
  doi      = {10.1109/TCYB.2016.2521767},
  keywords = {face recognition;learning (artificial intelligence);distance function;distance metric learning;face recognition;face verification;similarity learning;Cybernetics;Face;Face recognition;Learning systems;Measurement;Protocols;Taxonomy;Dimensionality reduction;distance metric learning;face recognition;face verification;similarity learning},
}

@Article{lee_brusilovsky_2017,
  author   = {Danielle H. Lee and Peter Brusilovsky},
  title    = {How to measure information similarity in online social networks: A case study of Citeulike},
  journal  = {Information Sciences},
  year     = {2017},
  volume   = {418},
  pages    = {46 - 60},
  issn     = {0020-0255},
  doi      = {http://dx.doi.org/10.1016/j.ins.2017.07.034},
  keywords = {Information similarity, Online social networks, Watching relations, Group membership, Social tags, Citeulike},
  url      = {http://www.sciencedirect.com/science/article/pii/S0020025517308538},
}

@Article{wu_etal_2017a,
  author   = {Wenmin Wu and Jianli Zhao and Chunsheng Zhang and Fang Meng and Zeli Zhang and Yang Zhang and Qiuxia Sun},
  title    = {Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding},
  journal  = {Knowledge-Based Systems},
  year     = {2017},
  volume   = {128},
  pages    = {71 - 77},
  issn     = {0950-7051},
  doi      = {http://dx.doi.org/10.1016/j.knosys.2017.04.011},
  keywords = {Context-aware recommendation, Tensor factorization, Regression tree, Context features selection},
  url      = {http://www.sciencedirect.com/science/article/pii/S0950705117301909},
}

@Article{chen_etal_2017b,
  author   = {Jinpeng Chen and Yu Liu and Guang Yang and Ming Zou},
  title    = {Inferring Tag Co-occurrence Relationship across Heterogeneous Social Networks},
  journal  = {Applied Soft Computing},
  year     = {2017},
  issn     = {1568-4946},
  doi      = {http://dx.doi.org/10.1016/j.asoc.2017.07.029},
  keywords = {Tag Co-occurrence, Link prediction, heterogeneous network, Flickr, weight path},
  url      = {http://www.sciencedirect.com/science/article/pii/S1568494617304404},
}

@Article{deng_etal_2017,
  author   = {Xiaofang Deng and Yuansheng Zhong and Linyuan Lü and Naixue Xiong and Chiho Yeung},
  title    = {A general and effective diffusion-based recommendation scheme on coupled social networks},
  journal  = {Information Sciences},
  year     = {2017},
  volume   = {417},
  pages    = {420 - 434},
  issn     = {0020-0255},
  doi      = {http://dx.doi.org/10.1016/j.ins.2017.07.021},
  keywords = {Social information, Cold start, Diffusion-based recommendation, Mass diffusion},
  url      = {http://www.sciencedirect.com/science/article/pii/S0020025517308368},
}

@Article{li_etal_2017,
  author   = {Jianguo Li and Yong Tang and Jiemin Chen},
  title    = {Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs},
  journal  = {Physica A: Statistical Mechanics and its Applications},
  year     = {2017},
  volume   = {483},
  pages    = {398 - 411},
  issn     = {0378-4371},
  doi      = {http://dx.doi.org/10.1016/j.physa.2017.04.121},
  keywords = {Regularized matrix factorization, Collaborative filtering, Tag, Diffusion, Tripartite graphs},
  url      = {http://www.sciencedirect.com/science/article/pii/S0378437117304107},
}

@Article{2015IEITI..98..902M,
  author  = {{Ma}, T. and {Zhou}, J. and {Tang}, M. and {Tian}, Y. and {Al-Dhelaan}, A. and {Al-Rodhaan}, M. and {Lee}, S.},
  title   = {{Social Network and Tag Sources Based Augmenting Collaborative Recommender System}},
  journal = {IEICE Transactions on Information and Systems},
  year    = {2015},
  volume  = {98},
  pages   = {902-910},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl  = {http://adsabs.harvard.edu/abs/2015IEITI..98..902M},
  doi     = {10.1587/transinf.2014EDP7283},
}

@Article{arnaboldi_etal_2017,
  author   = {Valerio Arnaboldi and Mattia G. Campana and Franca Delmastro and Elena Pagani},
  title    = {A personalized recommender system for pervasive social networks},
  journal  = {Pervasive and Mobile Computing},
  year     = {2017},
  volume   = {36},
  pages    = {3 - 24},
  issn     = {1574-1192},
  note     = {Special Issue on Pervasive Social Computing},
  doi      = {http://dx.doi.org/10.1016/j.pmcj.2016.08.010},
  keywords = {Pervasive content sharing, Mobile social networks, Opportunistic networks, Personalized recommender systems},
  url      = {http://www.sciencedirect.com/science/article/pii/S1574119216301365},
}

@Article{najafabadi_etal_2017,
  author   = {Maryam Khanian Najafabadi and Mohd Naz'ri Mahrin and Suriayati Chuprat and Haslina Md Sarkan},
  title    = {Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data},
  journal  = {Computers in Human Behavior},
  year     = {2017},
  volume   = {67},
  pages    = {113 - 128},
  issn     = {0747-5632},
  doi      = {http://dx.doi.org/10.1016/j.chb.2016.11.010},
  keywords = {Recommender systems, Collaborative filtering, Sparsity problem, Association rules mining, Clustering, Implicit feedback},
  url      = {http://www.sciencedirect.com/science/article/pii/S0747563216307518},
}

@Article{mezghani_etal_2017,
  author   = {Manel Mezghani and André Péninou and Corinne Amel Zayani and Ikram Amous and Florence Sèdes},
  title    = {Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information},
  journal  = {Data \& Knowledge Engineering},
  year     = {2017},
  volume   = {108},
  pages    = {15 - 29},
  issn     = {0169-023X},
  doi      = {http://dx.doi.org/10.1016/j.datak.2016.12.003},
  keywords = {User interests, Tagging behaviour, Resource, Indexation, Social network, Adaptation, Indexing methods, Semi-structured data and XML},
  url      = {http://www.sciencedirect.com/science/article/pii/S0169023X16303718},
}

@Article{ji_shen_2015,
  author   = {Ke Ji and Hong Shen},
  title    = {Addressing cold-start: Scalable recommendation with tags and keywords},
  journal  = {Knowledge-Based Systems},
  year     = {2015},
  volume   = {83},
  pages    = {42 - 50},
  issn     = {0950-7051},
  abstract = {Abstract Cold start problem for new users and new items is a major challenge facing most collaborative filtering systems. Existing methods to collaborative filtering (CF) emphasize to scale well up to large and sparse dataset, lacking of scalable approach to dealing with new data. In this paper, we consider a novel method for alleviating the problem by incorporating content-based information about users and items, i.e., tags and keywords. The user-item ratings imply the relevance of users’ tags to items’ keywords, so we convert the direct prediction on the user-item rating matrix into the indirect prediction on the tag-keyword relation matrix that adopts to the emergence of new data. We first propose a novel neighborhood approach for building the tag-keyword relation matrix based on the statistics of tag-keyword pairs in the ratings. Then, with the relation matrix, we propose a 3-factor matrix factorization model over the rating matrix, for learning every user’s interest vector for selected tags and every item’s correlation vector for extracted keywords. Finally, we integrate the relation matrix with the two kinds of vectors to make recommendations. Experiments on real dataset demonstrate that our method not only outperforms other state-of-the-art \{CF\} algorithms for historical data, but also has good scalability for new data. },
  doi      = {https://doi.org/10.1016/j.knosys.2015.03.008},
  keywords = {Recommender systems, Matrix factorization, Tag-keyword, Cold start, Scalability},
  url      = {http://www.sciencedirect.com/science/article/pii/S0950705115001008},
}

@Article{Kalantidis2011,
  author   = {Kalantidis, Yannis and Tolias, Giorgos and Avrithis, Yannis and Phinikettos, Marios and Spyrou, Evaggelos and Mylonas, Phivos and Kollias, Stefanos},
  title    = {VIRaL: Visual Image Retrieval and Localization},
  journal  = {Multimedia Tools and Applications},
  year     = {2011},
  volume   = {51},
  number   = {2},
  pages    = {555--592},
  month    = {Jan},
  issn     = {1573-7721},
  abstract = {New applications are emerging every day exploiting the huge data volume in community photo collections. Most focus on popular subsets, e.g., images containing landmarks or associated to Wikipedia articles. In this work we are concerned with the problem of accurately finding the location where a photo is taken without needing any metadata, that is, solely by its visual content. We also recognize landmarks where applicable, automatically linking them to Wikipedia. We show that the time is right for automating the geo-tagging process, and we show how this can work at large scale. In doing so, we do exploit redundancy of content in popular locations---but unlike most existing solutions, we do not restrict to landmarks. In other words, we can compactly represent the visual content of all thousands of images depicting e.g., the Parthenon and still retrieve any single, isolated, non-landmark image like a house or a graffiti on a wall. Starting from an existing, geo-tagged dataset, we cluster images into sets of different views of the same scene. This is a very efficient, scalable, and fully automated mining process. We then align all views in a set to one reference image and construct a 2D scene map. Our indexing scheme operates directly on scene maps. We evaluate our solution on a challenging one million urban image dataset and provide public access to our service through our online application, VIRaL.},
  day      = {01},
  doi      = {10.1007/s11042-010-0651-7},
  url      = {https://doi.org/10.1007/s11042-010-0651-7},
}

@Article{kim_etal_2010,
  author   = {Heung-Nam Kim and Ae-Ttie Ji and Inay Ha and Geun-Sik Jo},
  title    = {Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation},
  journal  = {Electronic Commerce Research and Applications},
  year     = {2010},
  volume   = {9},
  number   = {1},
  pages    = {73 - 83},
  issn     = {1567-4223},
  note     = {Special Issue: Social Networks and Web 2.0},
  abstract = {We propose a collaborative filtering method to provide an enhanced recommendation quality derived from user-created tags. Collaborative tagging is employed as an approach in order to grasp and filter users’ preferences for items. In addition, we explore several advantages of collaborative tagging for data sparseness and a cold-start user. These applications are notable challenges in collaborative filtering. We present empirical experiments using a real dataset from del.icio.us. Experimental results show that the proposed algorithm offers significant advantages both in terms of improving the recommendation quality for sparse data and in dealing with cold-start users as compared to existing work.},
  doi      = {http://dx.doi.org/10.1016/j.elerap.2009.08.004},
  keywords = {Collaborative tagging, Collaborative filtering, Recommender system},
  url      = {http://www.sciencedirect.com/science/article/pii/S1567422309000544},
}

@InProceedings{mustafa_etal_2017,
  author    = {N. Mustafa and A. O. Ibrahim and A. Ahmed and A. Abdullah},
  title     = {Collaborative filtering: Techniques and applications},
  booktitle = {2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE)},
  year      = {2017},
  pages     = {1-6},
  month     = {Jan},
  abstract  = {During the last decade a huge amount of data have been shown and introduced in the Internet. Recommender systems are thus predicting the rating that a user would give to an item. Collaborative filtering (CF) techniques are the most popular and widely used by recommender systems technique, which utilize similar neighbors to generate recommendations. This paper provides the concepts, methods, applications and evaluations of the CF based on the literature review. The paper also highlights the discussion of the types of the recommender systems as general and types of CF such as; memory based, model based and hybrid model. In addition, this paper discusses how to choose an appropriate type of CF. The evaluation methods of the CF systems are also provided throughout the paper.},
  doi       = {10.1109/ICCCCEE.2017.7867668},
  keywords  = {Internet;collaborative filtering;recommender systems;CF techniques;Internet;collaborative filtering;hybrid model CF;memory based CF;model based CF;recommender systems;Collaboration;Information filters;Measurement;Collaborative filtering;Evaluation Metrics;hybrid model;memory based;model based;recommender systems},
}

@InProceedings{chen_etal_2017,
  author    = {C. Chen and Z. Xing and X. Wang},
  title     = {Unsupervised Software-Specific Morphological Forms Inference from Informal Discussions},
  booktitle = {2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)},
  year      = {2017},
  pages     = {450-461},
  month     = {May},
  abstract  = {Informal discussions on social platforms (e.g., Stack Overflow) accumulates a large body of programming knowledge in natural language text. Natural language process (NLP) techniques can be exploited to harvest this knowledge base for software engineering tasks. To make an effective use of NLP techniques, consistent vocabulary is essential. Unfortunately, the same concepts are often intentionally or accidentally mentioned in many different morphological forms in informal discussions, such as abbreviations, synonyms and misspellings. Existing techniques to deal with such morphological forms are either designed for general English or predominantly rely on domain-specific lexical rules. A thesaurus of software-specific terms and commonly-used morphological forms is desirable for normalizing software engineering text, but very difficult to build manually. In this work, we propose an automatic approach to build such a thesaurus. Our approach identifies software-specific terms by contrasting software-specific and general corpuses, and infers morphological forms of software-specific terms by combining distributed word semantics, domain-specific lexical rules and transformations, and graph analysis of morphological relations. We evaluate the coverage and accuracy of the resulting thesaurus against community-curated lists of software-specific terms, abbreviations and synonyms. We also manually examine the correctness of the identified abbreviations and synonyms in our thesaurus. We demonstrate the usefulness of our thesaurus in a case study of normalizing questions from Stack Overflow and CodeProject.},
  doi       = {10.1109/ICSE.2017.48},
  keywords  = {Dictionaries;Electronic publishing;Encyclopedias;Internet;Software engineering;Thesauri;Stack Overflow;abbreviation;morphological form;synonym;word embedding, rank2},
}

@Article{zhou_etal_2017,
  author          = {Zhou, D. and Wu, X. and Zhao, W. and Lawless, S. and Liu, J.},
  title           = {Exploring external knowledge base for personalized search in collaborative tagging systems},
  journal         = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
  year            = {2017},
  volume          = {201},
  pages           = {408-417},
  note            = {cited By},
  abstract        = {Alongside the enormous volume of user-generated content posted to World Wide Web, there exists a thriving demand for search personalization services, especially those utilizing collaborative tagging data. To provide personalized services, a user model is usually required. We address the setting adopted by the majority of previous work, where a user model consists solely of the user’s past information. We construct an augmented user model from a number of tags and documents. These resources are further processed according to the user’s past information by exploring external knowledge base. A novel generative model is proposed for user model generation. This model leverages recent advances in neural language models such as Word Embeddings with latent semantic models such as Latent Dirichlet Allocation. We further present a new query expansion method to facilitate the desired personalized retrieval. Experiments conducted by utilizing real-world collaborative tagging data show that the methods proposed in the current paper outperform several non-personalized methods as well as existing personalized search methods by utilizing user models solely constructed from usage histories. © 2017, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
  affiliation     = {School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, China; ADAPT Centre, Knowledge and Date Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland},
  author_keywords = {Collaborative tagging systems; Latent semantic models; Personalized search; Query expansion; Word embeddings},
  document_type   = {Book Chapter},
  doi             = {10.1007/978-3-319-59288-6_37},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021907207&doi=10.1007%2f978-3-319-59288-6_37&partnerID=40&md5=ffef3abfc73a7111bdfcfe1a632043df},
}

@Article{yin_etal_2007,
  author        = {Yin, P. and Zhang, M. and Li, X.},
  title         = {Recommending scientific literatures in a collaborative tagging environment},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2007},
  volume        = {4822 LNCS},
  pages         = {478-481},
  note          = {cited By},
  abstract      = {Recently, collaborative tagging has become popular in the web2.0 world. Tags can be helpful if used for the recommendation since they reflect characteristic content features of the resources. However, there are few researches which introduce tags into the recommendation. This paper proposes a tag-based recommendation framework for scientific literatures which models the user interests with tags and literature keywords. A hybrid recommendation algorithm is then applied which is similar to the user-user collaborative filtering algorithm except that the user similarity is measured based on the vector model of user keywords other than the rating matrix, and that the rating is not from the user but represented as user-item similarity computed with the dot-product-based similarity instead of the cosine-based similarity. Experiments show that our tag-based algorithm is better than the baseline algorithm and the extension of user model and dot-product-based similarity computation are also helpful. © Springer-Verlag Berlin Heidelberg 2007.},
  affiliation   = {School of Electronics Engineering and Computer, Science Peking University, Beijing, China},
  document_type = {Conference Paper},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38149029112&partnerID=40&md5=85878767c03acc02f3b4d2febbf0ae67},
}

@InProceedings{firan_etal_2007a,
  author    = {C. S. Firan and W. Nejdl and R. Paiu},
  title     = {The Benefit of Using Tag-Based Profiles},
  booktitle = {2007 Latin American Web Conference (LA-WEB 2007)},
  year      = {2007},
  pages     = {32-41},
  month     = {Oct},
  abstract  = {Collaborative tagging, i.e. the process of assigning metadata in the form of keywords to shared content by many users, has emerged as an important way to provide information about resources on the Web and elsewhere. Such keywords (tags) are used to enable the organization of information within personal information spaces, such as photo collections, but can also be shared, allowing browsing and searching with the help of tags attached by other users to information resources from the Web. Recent research has shown that such tag distributions stabilize over time and can be used to improve search on the Web. In this paper we are interested in another aspect, namely how they characterize the user and enable personalized recommendations. Using data from a frequently used music search portal, Last.fm, we analyze tag usage and statistics and investigate the use of tag-based user profiles in contrast to conventional user profiles based on song and track usage. We specify recommendation algorithms based on tag user profiles, and explore how collaborative filtering recommendations based on these tag profiles are different from recommendations based on song/track profiles. Finally, we describe a new search-based method, which uses tags to recommend songs interesting to a user, yielding substantially improved results. The paper finishes with a discussion of some future work to further improve tag-based search and recommendation in community Web sites.},
  doi       = {10.1109/LA-Web.2007.13},
  keywords  = {Internet;information resources;user interfaces;collaborative tagging;community Web sites;information resources;metadata;search-based method;shared content;tag-based profiles;Collaboration;Collaborative work;Filtering algorithms;Information filtering;Information resources;Portals;Recommender systems;Statistical analysis;Statistical distributions;Tagging},
}

@Book{surowiecki_2004,
  title  = {The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations},
  year   = {2004},
  author = {Surowiecki, James},
}

@Conference{halpin_etal_2006,
  author        = {Halpin, H. and Robu, V. and Shepherd, H.},
  title         = {The dynamics and semantics of collaborative tagging},
  year          = {2006},
  volume        = {209},
  note          = {cited By},
  abstract      = {The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classification schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from "popular" tagged sites on the social bookmarking site del.icio.us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.},
  affiliation   = {University of Edinburgh, 2 Buccleuch Place, Edinburgh, United Kingdom; Dutch Center for Mathematics and Computer Science, Kruislaan 413, Amsterdam, Netherlands; Princeton University, Wallace Hall, Princeton, NJ, United States},
  document_type = {Conference Paper},
  journal       = {CEUR Workshop Proceedings},
  keywords      = {rank2},
  page_count    = {10},
  source        = {Scopus},
  url           = {http://ceur-ws.org/Vol-209/saaw06-full01-halpin.pdf},
}

@InProceedings{giannakidou_etal_2008a,
  author    = {E. Giannakidou and V. Koutsonikola and A. Vakali and Y. Kompatsiaris},
  title     = {Co-Clustering Tags and Social Data Sources},
  booktitle = {2008 The Ninth International Conference on Web-Age Information Management},
  year      = {2008},
  pages     = {317-324},
  month     = {July},
  abstract  = {Under social tagging systems, a typical Web 2.0 application, users label digital data sources by using freely chosen textual descriptions (tags). Poor retrieval in the aforementioned systems remains a major problem mostly due to questionable tag validity and tag ambiguity. Earlier clustering techniques have shown limited improvements, since they were based mostly on tag co-occurrences. In this paper, a co-clustering approach is employed, that exploits joint groups of related tags and social data sources, in which both social and semantic aspects of tags are considered simultaneously. Experimental results demonstrate the efficiency and the beneficial outcome of the proposed approach in correlating relevant tags and resources.},
  doi       = {10.1109/WAIM.2008.61},
  keywords  = {Internet;identification technology;pattern clustering;Web 2.0 application;coclustering approach;social tagging systems;textual descriptions;Bibliographies;Data mining;Data structures;Informatics;Information management;Knowledge representation;Multimedia systems;Ontologies;Tagging;Telematics;Co-clustering;Semantic Similarity;Social Similarity;Social Tagging Systems},
}

@Conference{zhao_etal_2008,
  author          = {Zhao, S. and Du, N. and Nauerz, A. and Zhang, X. and Yuan, Q. and Fu, R.},
  title           = {Improved recommendation based on collaborative tagging behaviors},
  year            = {2008},
  pages           = {413-416},
  note            = {cited By},
  abstract        = {Considering the natural tendency of people to follow direct or indirect cues of other people's activities, collaborative filtering-based recommender systems often predict the utility of an item for a particular user according to previous ratings by other similar users. Consequently, effective searching for the most related neighbors is critical for the success of the recommendations. In recent years, collaborative tagging systems with social bookmarking as their key component from the suite of Web 2.0 technologies allow users to freely bookmark and assign semantic descriptions to various shared resources on the web. While the list of favorite web pages indicates the interests or taste of each user, the assigned tags can further provide useful hints about what a user thinks of the pages. In this paper, we propose a new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection. That is, two users could be considered similar not only if they rated the items similarly, but also if they have similar cognitions over these items. We tested TBCF on real-life datasets, and the experimental results show that our approach has significant improvement against the traditional cosine-based recommendation method while leveraging user input not explicitly targeting the recommendation system. Copyright 2008 ACM.},
  affiliation     = {IBM, China Research Laboratory, Beijing, 100094, China; Beijing University of Posts and Telecommunications, Beijing, 100876, China; IBM, Research and Development, Schoenacher Str. 220, Boeblingen, 71032, Germany},
  art_number      = {1378843},
  author_keywords = {Collaborative filtering; Recommendation; Tag; Web2.0},
  document_type   = {Conference Paper},
  doi             = {10.1145/1378773.1378843},
  journal         = {International Conference on Intelligent User Interfaces, Proceedings IUI},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-54949093673&doi=10.1145%2f1378773.1378843&partnerID=40&md5=483255bbd46c6ef8cc174f50bce55d4f},
}

@Conference{yahia_etal_2008,
  author        = {Yahia, S.A. and Benedikt, M. and Lakshmanan, L.V.S. and Stoyanovichy, J.},
  title         = {Efficient network aware search in collaborative tagging sites},
  year          = {2008},
  volume        = {1},
  number        = {1},
  pages         = {710-721},
  note          = {cited By},
  abstract      = {The popularity of collaborative tagging sites presents a unique opportunity to explore keyword search in a context where query results are determined by the opinion of a network of taggers related to a seeker. In this paper, we present the first in-depth study of network-aware search. We investigate efficient top-k processing when the score of an answer is computed as its popularity among members of a seeker's network. We argue that obvious adaptations of top-k algorithms are too space-intensive, due to the dependence of scores on the seeker's network. We therefore develop algorithms based on maintaining score upper-bounds. The global upper-bound approach maintains a single score upper-bound for every pair of item and tag, over the entire collection of users. The resulting bounds are very coarse. We thus investigate clustering seekers based on similar behavior of their networks. We show that finding the optimal clustering of seekers is intractable, but we provide heuristic methods that give substantial time improvements. We then give an optimization that can benefit smaller populations of seekers based on clustering of taggers. Our results are supported by extensive experiments on del.icio.us datasets. © 2008 VLDB Endowment.},
  affiliation   = {Yahoo Research, United States; Oxford University, United Kingdom; University of British Columbia, Canada; Columbia University, United States; Natural Sciences and Engineering Research Council of Canada, Canada; National Institute of Health, United States},
  document_type = {Article},
  journal       = {Proceedings of the VLDB Endowment},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84859178171&partnerID=40&md5=7ae1d5e57255f5c387a9e3289fd450e1},
}

@Conference{rader_wash_2008,
  author          = {Rader, E. and Wash, R.},
  title           = {Influences on tag choices in del.icio.us},
  year            = {2008},
  pages           = {239-248},
  note            = {cited By},
  abstract        = {Collaborative tagging systems have the potential to produce socially constructed information organization schemes. The effectiveness of tags for finding and re-finding information depends upon how individual users choose tags; however, influences on users' tag choices are poorly understood. We quantitatively test competing hypotheses from the literature concerning these choices,

repository: queirozfcom/auto-tagger

repo_name: queirozfcom/auto-tagger