text:
using data from del.icio.us (a collaborative tagging system for organizing web bookmarks) and a computer model of possible tag choice strategies. We find evidence that users choose tags in a pattern consistent with personal information management goals, rather than as a result of social influence. Copyright 2008 ACM.},
  affiliation     = {School of Information, University of Michigan, United States},
  author_keywords = {Collaborative tagging; Computer model; Information management; Logistic regression},
  document_type   = {Conference Paper},
  doi             = {10.1145/1460563.1460601},
  journal         = {Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77950856352&doi=10.1145%2f1460563.1460601&partnerID=40&md5=4a175d26aa26f641b712403e1851d032},
}

@InProceedings{wu_etal_2006,
  author    = {Wu, Xian and Zhang, Lei and Yu, Yong},
  title     = {Exploring Social Annotations for the Semantic Web},
  booktitle = {Proceedings of the 15th International Conference on World Wide Web},
  year      = {2006},
  series    = {WWW '06},
  pages     = {417--426},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1135839},
  doi       = {10.1145/1135777.1135839},
  isbn      = {1-59593-323-9},
  keywords  = {emergent semantics, semantic web, social annotation, social bookmarks, rank1},
  location  = {Edinburgh, Scotland},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/1135777.1135839},
}

@InProceedings{janik_kochut_2008,
  author    = {M. Janik and K. J. Kochut},
  title     = {Wikipedia in Action: Ontological Knowledge in Text Categorization},
  booktitle = {2008 IEEE International Conference on Semantic Computing},
  year      = {2008},
  pages     = {268-275},
  month     = {Aug},
  abstract  = {We present a new, ontology-based approach to the automatic text categorization. An important and novel aspect of this approach is that our categorization method does not require a training set, which is in contrast to the traditional statistical and probabilistic methods. In the presented method, the ontology, including the domain concepts organized into hierarchies of categories and interconnected by relationships, as well as instances and connections among them, effectively becomes the classifier. Our method focuses on (i) converting a text document into a thematic graph of entities occurring in the document, (ii) ontological classification of the entities in the graph, and (iii) determining the overall categorization of the thematic graph, and as a result, the document itself. In the presented experiments, we used an RDF ontology constructed from the full English version of Wikipedia. Our experiments, conducted on corpora of Reuters news articles, showed that our training-less categorization method achieved a very good overall accuracy.},
  doi       = {10.1109/ICSC.2008.53},
  keywords  = {Web sites;graph theory;ontologies (artificial intelligence);pattern classification;text analysis;RDF ontology;Reuters news articles;Wikipedia;ontological classification;ontological knowledge;text categorization;text document;thematic entities graph;Automotive engineering;Computer science;Distributed computing;Distributed information systems;Information analysis;Large-scale systems;Ontologies;Resource description framework;Text categorization;Wikipedia;ontology;text categorization;wikipedia ontology},
}

@Article{gao_etal_2009,
  author          = {Gao, Q. and Dai, Y. and Fu, K.},
  title           = {Improving personal tagging consistency through visualization of tag relevancy},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2009},
  volume          = {5621 LNCS},
  pages           = {326-335},
  note            = {cited By},
  abstract        = {Tagging has emerged as a new means of organizing information, but the inconsistency in tagging behaviors of users is a major drawback which degrades both information organization and retrieval performance. The current study aims to study how the intra-personal consistency of tagging can be improved by proper tag visualization. The effects of visualization of tag frequency and visualization of the relevancy among tags on personal tagging consistency are empirically tested and compared through an experiment with 39 participants. The results show that visualization of tag relevancy improves tagging consistency significantly and reduces mental workload simultaneously; visualization of tag frequency may alleviate perceived physical demand when tag relevancy is visualized. The findings provide clear and meaningful implications for system designers. © 2009 Springer Berlin Heidelberg.},
  affiliation     = {Department of Industrial Engineering, Tsinghua University, Beijing 100084, China},
  author_keywords = {Collaborative tagging systems; Consistency; Information visualization; Tag cloud; Tagging},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-02774-1_36},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-76649121497&doi=10.1007%2f978-3-642-02774-1_36&partnerID=40&md5=7ab96bceb814ac6d945237e26646decc},
}

@Conference{bischoff_etal_2009b,
  author          = {Bischoff, K. and Firan, C.S. and Paiu, R.},
  title           = {Deriving music theme annotations from user tags},
  year            = {2009},
  pages           = {1193-1194},
  note            = {cited By},
  abstract        = {Music theme annotations would be really beneficial for supporting retrieval, but are often neglected by users while annotating. Thus, in order to support users in tagging and to fill the gaps in the tag space, in this paper we develop algorithms for recommending theme annotations. Our methods exploit already existing user tags, the lyrics of music tracks, as well as combinations of both. We compare the results for our recommended theme annotations against genre and style recommendations - a much easier and already studied task. We evaluate the quality of our recommended tags against an expert ground truth data set. Our results are promising and provide interesting insights into possible extensions for music tagging systems to support music search. Copyright is held by the author/owner(s).},
  affiliation     = {L3S Research Center, Leibniz Universität Hannover, Appelstr. 9a, 30167 Hannover, Germany},
  author_keywords = {Collaborative tagging; High-level music descriptors; Metadata enrichment; Theme tag recommendations},
  document_type   = {Conference Paper},
  doi             = {10.1145/1526709.1526924},
  journal         = {WWW'09 - Proceedings of the 18th International World Wide Web Conference},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70450279739&doi=10.1145%2f1526709.1526924&partnerID=40&md5=ca87c99a5a38c80331c3292264733431},
}

@Article{bischoff_etal_2009a,
  author          = {Bischoff, K. and Firan, C.S. and Kadar, C. and Nejdl, W. and Paiu, R.},
  title           = {Automatically identifying tag types},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2009},
  volume          = {5678 LNAI},
  pages           = {31-42},
  note            = {cited By},
  abstract        = {Web 2.0 applications such as delicious, flickr or lastfm have recently become extremely popular and as a result, a large amount of semantically rich metadata produced by users becomes available and exploitable. Tag information can be used for many purposes (e.g. user profiling, recommendations, clustering etc), though the benefit of tags for search is by far the most discussed usage. Tag types differ largely across systems and previous studies showed that, while some tag type categories might be useful for some particular users when searching, they may not bring any benefit to others. The present paper proposes an approach which utilizes rule-based as well as model-based methods, in order to automatically identify exactly these different types of tags. We compare the automatic tag classification produced by our algorithms against a ground truth data set, consisting of manual tag type assignments produced by human raters. Experimental results show that our methods can identify tag types with high accuracy, thus enabling further improvement of systems making use of social tags. © 2009 Springer.},
  affiliation     = {L3S Research Center, Leibniz Universität Hannover, Appelstrasse 9a, Hannover 30167, Germany},
  author_keywords = {Classification; Collaborative tagging; Social media; Tag types},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-03348-3_7},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350340351&doi=10.1007%2f978-3-642-03348-3_7&partnerID=40&md5=871fd7a3ca26072cefdb867e8571b35a},
}

@Article{bischoff_etal_2009,
  author          = {Bischoff, K. and Firan, C.S. and Georgescu, M. and Nejdl, W. and Paiu, R.},
  title           = {Social knowledge-driven music hit prediction},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2009},
  volume          = {5678 LNAI},
  pages           = {43-54},
  note            = {cited By},
  abstract        = {What makes a song to a chart hit? Many people are trying to find the answer to this question. Previous attempts to identify hit songs have mostly focused on the intrinsic characteristics of the songs, such as lyrics and audio features. As social networks become more and more popular and some specialize on certain topics, information about users' music tastes becomes available and easy to exploit. In the present paper we introduce a new method for predicting the potential of music tracks for becoming hits, which instead of relying on intrinsic characteristics of the tracks directly uses data mined from a music social network and the relationships between tracks, artists and albums. We evaluate the performance of our algorithms through a set of experiments and the results indicate good accuracy in correctly identifying music hits, as well as significant improvement over existing approaches. © 2009 Springer.},
  affiliation     = {L3S Research Center, Leibniz Universität Hannover, Appelstrasse 9a, Hannover 30167, Germany},
  author_keywords = {Classification; Collaborative tagging; Hit songs; Social media},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-03348-3_8},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350339343&doi=10.1007%2f978-3-642-03348-3_8&partnerID=40&md5=fac26adc2e89cf5cddc965368d275efa},
}

@InProceedings{song_etal_2010,
  author    = {Jian Song and Liang He and Xin Lin},
  title     = {Improving the accuracy of Tagging Recommender System by Using Classification},
  booktitle = {2010 The 12th International Conference on Advanced Communication Technology (ICACT)},
  year      = {2010},
  volume    = {1},
  pages     = {387-391},
  month     = {Feb},
  abstract  = {Collaborative tagging system has become more and more popular and recently achieved widespread success due to flexibility and conceptual comprehensibility of tagging systems. Recommender system has the access to adopt tagging systems to achieve better performance. In this paper we consider that the items can be categorized into different classifications in which users show different interests. Here we adopt a two-step recommender method called TRSUC (Tagging Recommender Systems by Using Classification) which can be described as Inner-Class Recommender or Global Recommender in which we use tag as the intermediary entity between user and item. The experiment using MovieLens as dataset shows that we acquire better results than the recommender algorithms without classifying the items.},
  issn      = {1738-9445},
  keywords  = {groupware;pattern classification;recommender systems;MovieLens dataset;classification;collaborative tagging system;global recommender;inner-class recommender;tagging recommender system;Collaboration;Collaborative work;Filtering algorithms;Helium;Information resources;Information retrieval;Merchandise;Motion pictures;Recommender systems;Tagging;Classification;Collaborative Tagging;Recommender System},
}

@InProceedings{kammergruber_etal_2010,
  author    = {W. C. Kammergruber and M. Viermetz and K. Ehms and M. Langen},
  title     = {Using association rules for discovering tag bundles in social tagging data},
  booktitle = {2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM)},
  year      = {2010},
  pages     = {414-419},
  month     = {Oct},
  abstract  = {Social tagging has become very popular with the rise of Web 2.0, making it an important piece of the puzzle that makes up the web phenomenon. In contrast to more elaborate ways of organizing resources, such as taxonomies or ontologies, tagging is very easy to use and understand. Because of its simplicity tagging does not create explicit, formalized, structures. By applying a well-established data mining algorithm we show how common structures in form of tag bundles (a set of related tags) can automatically be derived from a bag of tags. Based on these computed tag bundles tagging applications can assist users with improved user interfaces by introducing user based semantic labelling. For navigating and searching his or her tagged information space there are certainly better user interface elements possible than a tag cloud or a plain list of tags ordered by frequency. We propose that combining frequently associated tags into a tag bundle is such a way. We have tested our approach on a data set gained from Delicious and evaluated it by utilizing the Normalized Google Distance.},
  doi       = {10.1109/CISIM.2010.5643503},
  keywords  = {data mining;identification technology;social networking (online);user interfaces;Web 2.0;association rules;data mining algorithm;normalized Google distance;ontologies;social tagging data;tag bundles;tag bundles tagging applications;taxonomies;user based semantic labelling;user interface elements;Association rules;Context;Google;Organizing;Semantics;Tagging;association rule mining;data mining;folksonomy;personal information management;social tagging;tag bundle},
}

@InProceedings{giannakidou_etal_2010,
  author    = {E. Giannakidou and V. Koutsonikola and A. Vakali and I. Kompatsiaris},
  title     = {Exploring temporal aspects in user-tag co-clustering},
  booktitle = {11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10},
  year      = {2010},
  pages     = {1-4},
  month     = {April},
  abstract  = {Tagging environments have become an interesting topic of research lately, focused mainly on clustering approaches, in order to extract emergent patterns that are derived from tag similarity and involve tag relations or user interconnections. Apart from tag similarity, an interesting parameter to be analyzed during the clustering/mining process in such data is the actual time that each tagging activity occurred. Indeed, holding a temporal dimension unfolds macroscopic and microscopic views of tagging, highlights links between objects for specific time periods and, in general, lets us observe how the users' tagging activity changes over time. In this article, we propose a time-aware user/tag clustering approach, which groups together similar users and tags that are very “active” during the same time periods. Emphasis is given on using varying time scales, so that we distinguish between clusters that are robust at many time scales and clusters that are somehow occasional, i.e. they emerge, only at a specific time period.},
  issn      = {2158-5873},
  keywords  = {identification technology;pattern clustering;user interfaces;tagging environments;temporal aspects;time-aware user/tag clustering;user interconnections;user-tag co-clustering;Media;Technical Activities Guide - TAG},
  url       = {http://ieeexplore.ieee.org/document/5617671/},
}

@InProceedings{krause_etal_2008,
  author    = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd},
  title     = {The Anti-social Tagger: Detecting Spam in Social Bookmarking Systems},
  booktitle = {Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web},
  year      = {2008},
  series    = {AIRWeb '08},
  pages     = {61--68},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1451998},
  doi       = {10.1145/1451983.1451998},
  isbn      = {978-1-60558-159-0},
  keywords  = {folksonomy, spam detection, web 2.0},
  location  = {Beijing, China},
  numpages  = {8},
  url       = {http://doi.acm.org/10.1145/1451983.1451998},
}

@Conference{ley_seitlinger_2010,
  author          = {Ley, T. and Seitlinger, P.},
  title           = {A cognitive perspective on emergent semantics in collaborative tagging: The basic level effect},
  year            = {2010},
  volume          = {590},
  pages           = {13-18},
  note            = {cited By},
  abstract        = {Researching the emergence of semantics in social systems needs to take into account how users process information in their cognitive system. We report results of an experimental study in which we examined the interaction between individual expertise and the basic level advantage in collaborative tagging. The basic level advantage describes availability in memory of certain preferred levels of taxonomic abstraction when categorizing objects and has been shown to vary with level of expertise. In the study, groups of students tagged internet resources for a 10-week period. We measured the availability of tags in memory with an association test and a relevance rating and found a basic level advantage for tags from more general as opposed to specific levels of the taxonomy. An interaction with expertise also emerged. Contrary to our expectations, groups that spent less time to develop a shared understanding shifted to more specific levels as compared to groups that spent more time on a topic. We attribute this to impaired collaboration in the groups. We discuss implications for personalized tag and resource recommendations.},
  affiliation     = {Know-Center, Inffeldgasse 21a, 8010 Graz, Austria; Cognitive Science Section, University of Graz, Universitätsplatz 2, 8010 Graz, Austria},
  author_keywords = {Categorization; Personalized recommendation; Tagging},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {http://ceur-ws.org/Vol-590/sasweb10_2.pdf},
}

@Article{floeck_etal_2010,
  author          = {Floeck, F. and Putzke, J. and Steinfels, S. and Fischbach, K. and Schoder, D.},
  title           = {Imitation and quality of tags in social bookmarking systems - Collective intelligence leading to folksonomies},
  journal         = {Advances in Intelligent and Soft Computing},
  year            = {2010},
  volume          = {76},
  pages           = {75-91},
  note            = {cited By},
  abstract        = {Social bookmarking platforms often allow users to see a list of tags that have been used previously for the webpage they are currently bookmarking, and from which they can select. In this paper, the authors analyze the influences of this feature on the tag categorizations resulting from the collaborative tagging effort. The main research goal is to show how the interface design of social bookmarking systems can influence the quality of the collective output of their users. Findings from a joint research project with the largest Russian social bookmarking site BobrDobr.ru suggest that if social bookmarking systems allow users to view the most popular tags, the overall variation of keywords used that are assigned to websites by all users decreases. © 2010 Springer-Verlag Berlin Heidelberg.},
  affiliation     = {University of Cologne, Department of Information Systems and Information Management, Pohligstr. 1, 50969 Cologne (Köln), Germany},
  author_keywords = {Collaborative Tagging; Collective Intelligence; Folksonomies; Shared Knowledge; Social-Bookmarking-Systems},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-14481-3_7},
  source          = {Scopus},
  url             = {https://link.springer.com/chapter/10.1007%2F978-3-642-14481-3_7},
}

@Conference{darvish_chin_2010,
  author          = {Darvish, S. and Chin, A.},
  title           = {Dealing with the video tidal wave: The relevance of expertise for video tagging},
  year            = {2010},
  pages           = {289-290},
  note            = {cited By},
  abstract        = {The vast amounts of video that need to be tagged preclude the exclusive use of professional indexers. Thus a significant amount of video will need to be tagged by non-experts. Are the tags created by experts demonstrably superior to those of non-experts, and when non-experts have to be used for tagging, is it better to rely on tags created by those who upload videos or on others who watch the videos? Two related studies were carried out, the first where domain experts and others tagged videos, and the second where experts and others rated the relevance of tags that had been assigned to videos in the first study. Expert tags were judged to be more relevant by both experts and non-experts, with non-expert viewers also creating significantly better tags than did owners (i.e., the people who uploaded the videos). While significant differences were observed, the mean overall judged relevance of tags was relatively low, even for experts. Thus there seems to be considerable scope for the use of tag recommendation systems and other tools that can make the tagging process more consistent.},
  affiliation     = {Research and Innovation, George Brown College, 160 Kendal Avenue, Toronto, ON M5T 2T9, Canada; Nokia Research Center, Building 2, No. 5 Donghuan Zhonglu, Beijing, 100176, China},
  author_keywords = {Categorization; Classification; Collaborative tagging; Tagging systems; Video annotation},
  document_type   = {Conference Paper},
  doi             = {10.1145/1810617.1810679},
  journal         = {HT'10 - Proceedings of the 21st ACM Conference on Hypertext and Hypermedia},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954921226&doi=10.1145%2f1810617.1810679&partnerID=40&md5=51a6ff324fcb52ebbcc8597af6bedb0b},
}

@InProceedings{fenza_etal_2011,
  author    = {G. Fenza and V. Loia and S. Senatore},
  title     = {Agent-assisted tagging aimed at folkonomy-based information retrieval},
  booktitle = {2011 IEEE Symposium on Intelligent Agent (IA)},
  year      = {2011},
  pages     = {1-8},
  month     = {April},
  abstract  = {The wide diffusion of community tagging sites and related folksonomies has made the knowledge discovery and retrieval still much more urgent topic. If tagging systems allow users to add freely keywords to web resources, clicking on a tag has the side effect of a tag-based query, since enables the users to explore related content. The collective knowledge expressed though user-annotated data has a big potential, but needs to be filtered in a digest form so that the search result better reflects the users' preferences and actual aims. Starting from these considerations, our work presents an agent-based approach for a scalable semi-automatic generation of annotation tags, personalized on each user's preferences and tastes. Primary is the role of agents which assist users in the tagging activities as well as the retrieval of resources related to their interest. A user-friendly interface proposes an integrated one-shot view for interacting with a tagging system.},
  doi       = {10.1109/IA.2011.5953613},
  keywords  = {Internet;Web sites;data mining;human computer interaction;multi-agent systems;query processing;user interfaces;Web resources;agent-assisted tagging system;annotation tag semi-automatic generation;community tagging sites;folkonomy-based information retrieval;knowledge discovery;knowledge retrieval;tag-based query;user-annotated data;user-friendly interface;Communities;Computational modeling;Context;Lattices;Tagging;Web pages;Fuzzy Formal Concept Analysis (FFCA);Multi-agent system;Personalization;Tagging},
}

@Article{lu_etal_2011,
  author   = {C. Lu and X. Hu and J. r. Park},
  title    = {Exploiting the Social Tagging Network for Web Clustering},
  journal  = {IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans},
  year     = {2011},
  volume   = {41},
  number   = {5},
  pages    = {840-852},
  month    = {Sept},
  issn     = {1083-4427},
  abstract = {Social tagging is a major characteristic of Web 2.0. A social tagging system can be modeled with a tripartite network of users, resources, and tags. In this paper, we investigate how to enhance Web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method called “Tripartite Clustering” which clusters the three types of nodes (resources, users, and tags) simultaneously by only utilizing the links in the social tagging network. We also investigate two other approaches to exploit social tagging for clustering with K-means and Link K-means. All the clustering methods are experimented on a real-world social tagging data set sampled from del.icio.us. The clustering results are evaluated against a human-maintained Web directory. The experimental results show that the social tagging network is a very useful information source for document clustering. All social-annotation-based clustering methods can significantly improve the performance of content-based clustering. Compared to social-annotation-based K-means and Link K-means, Tripartite Clustering achieves equivalent or better performance and produces more useful information.},
  doi      = {10.1109/TSMCA.2011.2157128},
  keywords = {Internet;document handling;information resources;pattern clustering;Link K-means;Web 2.0;Web clustering;content based clustering;document clustering;human maintained Web directory;information source;social annotation based K-means;social annotation based clustering method;social tagging data set;social tagging network;tripartite clustering;tripartite network;Clustering algorithms;Clustering methods;Measurement;Semantics;Tagging;Vectors;Web pages;Clustering methods;social annotation;social tagging;tripartite network},
}

@InProceedings{yang_toderici_2011,
  author    = {W. Yang and G. Toderici},
  title     = {Discriminative tag learning on YouTube videos with latent sub-tags},
  booktitle = {CVPR 2011},
  year      = {2011},
  pages     = {3217-3224},
  month     = {June},
  abstract  = {We consider the problem of content-based automated tag learning. In particular, we address semantic variations (sub-tags) of the tag. Each video in the training set is assumed to be associated with a sub-tag label, and we treat this sub-tag label as latent information. A latent learning framework based on LogitBoost is proposed, which jointly considers both the tag label and the latent sub-tag label. The latent sub-tag information is exploited in our framework to assist the learning of our end goal, i.e., tag prediction. We use the cowatch information to initialize the learning process. In experiments, we show that the proposed method achieves significantly better results over baselines on a large-scale testing video set which contains about 50 million YouTube videos.},
  doi       = {10.1109/CVPR.2011.5995402},
  issn      = {1063-6919},
  keywords  = {content-based retrieval;learning (artificial intelligence);social networking (online);video retrieval;LogitBoost;YouTube videos;content-based automated tag learning;discriminative tag learning;latent learning framework;latent sub-tag label;tag label;Feature extraction;Semantics;Support vector machines;Tagging;Training;Videos;YouTube},
}

@Conference{das_etal_2011,
  author          = {Das, M. and Das, G. and Hristidis, V.},
  title           = {Leveraging collaborative tagging for web item design},
  year            = {2011},
  pages           = {538-546},
  note            = {cited By},
  abstract        = {The popularity of collaborative tagging sites has created new challenges and opportunities for designers of web items, such as electronics products, travel itineraries, popular blogs, etc. An increasing number of people are turning to online reviews and user-specified tags to choose from among competing items. This creates an opportunity for designers to build items that are likely to attract desirable tags when published. In this paper, we consider a novel optimization problem: given a training dataset of existing items with their user-submitted tags, and a query set of desirable tags, design the k best new items expected to attract the maximum number of desirable tags. We show that this problem is NP-Complete, even if simple Naive Bayes Classifiers are used for tag prediction. We present two principled algorithms for solving this problem: (a) an exact "two-tier" algorithm (based on top-k querying techniques), which performs much better than the naive brute-force algorithm and works well for moderate problem instances, and (b) a novel polynomialtime approximation algorithm with provable error bound for larger problem instances. We conduct detailed experiments on synthetic and real data crawled from the web to evaluate the efficiency and quality of our proposed algorithms. Copyright 2011 ACM.},
  affiliation     = {Department of Computer Science and Engineering, University of Texas, Arlington, United States; School of Computing and Information Sciences, Florida International University, United States},
  author_keywords = {Collaborative tagging; Item design; Naive bayes; Optimization},
  document_type   = {Conference Paper},
  doi             = {10.1145/2020408.2020493},
  journal         = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052690914&doi=10.1145%2f2020408.2020493&partnerID=40&md5=a195c6db2a96937120ae37fe6287e17a},
}

@Article{kichou_etal_2011,
  author          = {Kichou, S. and Mellah, H. and Amghar, Y. and Dahak, F.},
  title           = {Tags weighting based on user profile},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2011},
  volume          = {6890 LNCS},
  pages           = {206-216},
  note            = {cited By},
  abstract        = {The 'Collaborative Tagging' is gaining popularity on Web 2.0, this new generation of Web which makes user reader/writer. The 'Tagging' is a mean for users to express themselves freely through additions of label called 'Tags' to shared resources. One of the problems encountered in current tagging systems is to define the most appropriate tag for a resource. Tags are typically listed in order of popularity, as del-icio-us. But the popularity of the tag does not always reflect its importance and representativeness for the resource to which it is associated. Starting from the assumptions that the same tag for a resource can take different meanings for different users, and a tag from a knowledgeable user would be more important than a tag from a novice user, we propose an approach for weighting resource's tags based on user profile. For this we define a user model for his integration in the tag weight calculation and a formula for this calculation, based on three factors namely the user, the degree of approximation between his interest centers and the resource field, expertise and personal assessment for tags associated to the resource. A resource descriptor containing the best tags is created. © 2011 Springer-Verlag.},
  affiliation     = {Research Center on Scientific and Technical Information (CERIST) Benaknoun, Algiers, Algeria; INSA of Lyon, Computing on Images and Information Systems Laboratory, Lyon, France; National Hight School of Computer Science, ESI, Oued Smar, Algiers, Algeria},
  author_keywords = {Collaborative Tagging; Information retrieval; User profile},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-23620-4_24},
  source          = {Scopus},
  url             = {https://link.springer.com/chapter/10.1007%2F978-3-642-23620-4_24},
}

@Conference{gohr_etal_2011,
  author          = {Gohr, A. and Hinneburg, A. and Spiliopoulou, M. and Usbeck, R.},
  title           = {On the distinctiveness of tags in collaborative tagging systems},
  year            = {2011},
  note            = {cited By},
  abstract        = {We study in a quantitative way whether the most popular tags in a collaborative tagging system are distinctive features when looking at the underlying content. For any set of annotations being helpful in searching, this property must necessarily hold to a strong degree. Our initial experiments show that the most frequent tags in CiteULike are distinctive features, despite the process of annotating documents is not centrally coordinated nor correction mechanisms like in a Wiki-system are used. © 2011 ACM.},
  affiliation     = {Computer Science, Martin-Luther-Univerisity, Halle, Germany; Computer Science, Otto-von-Guericke-University, Magdeburg, Germany},
  author_keywords = {collaborative information systems; tagging; text mining},
  document_type   = {Conference Paper},
  doi             = {10.1145/1988688.1988760},
  journal         = {ACM International Conference Proceeding Series},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960605362&doi=10.1145%2f1988688.1988760&partnerID=40&md5=ef7d11f6fa78d00949aa1d7500b8eabd},
}

@InProceedings{koh_etal_2012,
  author    = {J. L. Koh and N. Shongwe and C. W. Cho},
  title     = {A multi-level hierarchical index structure for supporting efficient similarity search on tag sets},
  booktitle = {2012 Sixth International Conference on Research Challenges in Information Science (RCIS)},
  year      = {2012},
  pages     = {1-12},
  month     = {May},
  abstract  = {Social communication websites has been an emerging type of a Web service that helps users to share their resources. For providing efficient similarity search of tag set in a social tagging system, we propose a multi-level hierarchical index structure to group similar tag sets. Not only the algorithms of similarity searches of tag sets, but also the algorithms of deletion and updating of tag sets by using the constructed index structure are provided. Furthermore, we define a modified hamming distance function on tag sets, which consider the semantically relatedness when comparing the members for evaluating the similarity of two tag sets. This function is more applicable to evaluate the similarity search of two tag sets. A systematic performance study is performed to verify the effectiveness and the efficiency of the proposed strategies. The experiment results show that the proposed MHIB approach further improves the pruning effect of the previous work which constructs a two-level index structure. Especially, the MHIB approach is well scalable with respect to the three parameters when using either the hamming distance or the modified hamming distance for similarity measure. Although the insertion operation of the MHIB approach requires higher cost than the naïve method, with the assistant of the constructed inverted list of clusters, it performs faster than the previous work. Besides, the cost of performing deletion operation by using the MHIB approach is much less than the other two approaches and so is the update operation.},
  doi       = {10.1109/RCIS.2012.6240436},
  issn      = {2151-1349},
  keywords  = {Web services;information retrieval;resource allocation;social networking (online);MHIB approach;Web service;efficient similarity search;modified hamming distance function;multilevel hierarchical index structure;resource sharing;similar tag set grouping;social communication Web sites;social tagging system;tag sets deletion;two-level index structure;Hamming distance;Indexing;Search problems;Tagging;Transaction databases;Upper bound;Social tagging;index structure;similarity search},
}

@InProceedings{zeng_etal_2013,
  author    = {G. Zeng and P. Luo and E. Chen and M. Wang},
  title     = {From Social User Activities to People Affiliation},
  booktitle = {2013 IEEE 13th International Conference on Data Mining},
  year      = {2013},
  pages     = {1277-1282},
  month     = {Dec},
  abstract  = {This study addresses the problem of inferring users' employment affiliation information from social activities. It is motivated by the applications which need to monitoring and analyzing the social activities of the employees from a given company, especially their social tracks related to the work and business. It definitely helps to better understand their needs and opinions towards certain business area, so that the account sales targeting these customers in the given company can adjust the sales strategies accordingly. Specifically, in this task we are given a snapshot of a social network and some labeled social users who are the employees of a given company. Our goal is to identify more users from the same company. We formulate this problem as a task of classifying nodes over a graph, and develop a Supervised Label Propagation model. It naturally incorporates the rich set of features for social activities, models the networking effect by label propagation, and learns the feature weights so that the labels are propagated to the right users. To validate its effectiveness, we show our case studies on identifying the employees of "China Telecom" and "China Unicom" from Sina Weibo. The experimental results show that our method significantly outperforms the compared baseline ones.},
  doi       = {10.1109/ICDM.2013.101},
  issn      = {1550-4786},
  keywords  = {learning (artificial intelligence);social networking (online);China Telecom;China Unicom;social network;social user activities;supervised label propagation model;Companies;Equations;Media;Social network services;Telecommunications;Vectors},
}

@InProceedings{saari_etal_2013,
  author    = {P. Saari and M. Barthet and G. Fazekas and T. Eerola and M. Sandler},
  title     = {Semantic models of musical mood: Comparison between crowd-sourced and curated editorial tags},
  booktitle = {2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)},
  year      = {2013},
  pages     = {1-6},
  month     = {July},
  abstract  = {Social media services such as Last.fm provide crowd-sourced mood tags which are a rich but often noisy source of information. In contrast, editorial annotations from production music libraries are meant to be incisive in nature. We compare the efficiency of these two data sources in capturing semantic information on mood expressed by music. First, a semantic computing technique devised for mood-related tags in large datasets is applied to Last.fm and I Like Music (ILM) corpora separately (250,000 tracks each). The resulting semantic estimates are then correlated with listener ratings of arousal, valence and tension. High correlations (Spearman's rho) are found between the track positions in the dimensional mood spaces and listener ratings using both data sources (0.60 <; rs <; 0.70). In addition, the use of curated editorial data provides a statistically significant improvement compared to crowd-sourced data for predicting moods perceived in music.},
  doi       = {10.1109/ICMEW.2013.6618436},
  keywords  = {behavioural sciences;correlation methods;music;semantic Web;social networking (online);I Like Music corpora;ILM corpora;Last.fm corpora;Spearman rho;crowd-sourced data;crowd-sourced mood tags;curated editorial tags;data sources;editorial annotations;listener ratings;mood-related tags;musical mood;noisy information source;production music libraries;semantic computing technique;semantic information;semantic models;social media services;Correlation;Editorials;Mood;Production;Semantics;Vectors;Vocabulary;Semantic computing;affective circumplex transformation;dimensional emotion model;music moods},
}

@InProceedings{chen_etal_2013,
  author    = {S. Chen and S. Owusu and L. Zhou},
  title     = {Social Network Based Recommendation Systems: A Short Survey},
  booktitle = {2013 International Conference on Social Computing},
  year      = {2013},
  pages     = {882-885},
  month     = {Sept},
  abstract  = {This paper examines the background of the recommender systems and the state-of-art technologies in this current research area. We examined the needs for recommendation systems and the enhanced performance by using the additional social network information. We compared different types of social network information and how they are used to improve the recommendation systems with a few examples. We also discussed different evaluation methods that are important to measure the performances of a recommendation system. We concluded that the recommendation system is a very important research area and could be applied in many domains. We summarized different measures towards evaluating a recommendation system especially those incorporating the social network information.},
  doi       = {10.1109/SocialCom.2013.134},
  keywords  = {recommender systems;social networking (online);social network based recommendation systems;social network information;state-of-art technologies;Accuracy;Bayes methods;Collaboration;Measurement;Prediction algorithms;Recommender systems;Social network services;Algorithms;Artificial Intelligence;Recommendation Systems},
}

@Article{figueiredo_etal_2013,
  author   = {Flavio Figueiredo and Henrique Pinto and Fabiano Belém and Jussara Almeida and Marcos Gonçalves and David Fernandes and Edleno Moura},
  title    = {Assessing the quality of textual features in social media},
  journal  = {Information Processing \& Management},
  year     = {2013},
  volume   = {49},
  number   = {1},
  pages    = {222 - 247},
  issn     = {0306-4573},
  doi      = {http://dx.doi.org/10.1016/j.ipm.2012.03.003},
  keywords = {Web 2.0, Textual features, Information quality, Social media},
  url      = {http://www.sciencedirect.com/science/article/pii/S0306457312000465},
}

@InProceedings{demir_oezguer_2014,
  author    = {H. Demir and A. Özgür},
  title     = {Improving Named Entity Recognition for Morphologically Rich Languages Using Word Embeddings},
  booktitle = {2014 13th International Conference on Machine Learning and Applications},
  year      = {2014},
  pages     = {117-122},
  month     = {Dec},
  abstract  = {In this paper, we addressed the Named Entity Recognition (NER) problem for morphologically rich languages by employing a semi-supervised learning approach based on neural networks. We adopted a fast unsupervised method for learning continuous vector representations of words, and used these representations along with language independent features to develop a NER system. We evaluated our system for the highly inflectional Turkish and Czech languages. We improved the state-of-the-art F-score obtained for Turkish without using gazetteers by 2.26% and for Czech by 1.53%. Unlike the previous state-of-the-art systems developed for these languages, our system does not make use of any language dependent features. Therefore, we believe it can easily be applied to other morphologically rich languages.},
  doi       = {10.1109/ICMLA.2014.24},
  keywords  = {natural language processing;neural nets;unsupervised learning;word processing;Czech languages;F-score;NER problem;NER system;Turkish languages;continuous word vector representations;language independent features;morphologically rich languages;named entity recognition problem;neural networks;semisupervised learning approach;unsupervised learning method;word embeddings;Cities and towns;Context;Measurement;Neural networks;Semisupervised learning;Training;Vectors;Czech NER;Named Entity Recognition;Skip-gram;Turkish NER;Word Embeddings, rank1},
}

@InProceedings{shan_etal_2015,
  author    = {Lili Shan and Chengjie Sun and Lei Lin and Ming Liu and Xiaolong Wang and Bingquan Liu},
  title     = {Evaluating tag quality for blogger modelling via topic models},
  booktitle = {2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)},
  year      = {2015},
  pages     = {1770-1776},
  month     = {Aug},
  abstract  = {With the permission of annotating blog posts with tags, tags has become one of the most important resources used to describe blogger features. However, due to the irregular quality of tags, not all tags are appropriate for representing blogger's preferences. Poor tags or spam tags confuse the actual user's preferences and spam terms, thus they should be detected before they are directly used to tag bloggers. A detailed quantitative analysis on the categories of tag spam in the blogosphere is presented in this paper. Taking advantage of abundant text contents in blog posts and the relatively stable semantic relationship between tags and their target posts, an unsupervised approach based on topic models is proposed to evaluate tag quality for blogger modelling in the blogosphere. The latent interest topics of a blogger are mined out through Latent Dirichlet Allocation (LDA) topic modeling. The blog post of the blogger is represented as a distribution over latent topics and a latent topic is a distribution over words of the vocabulary. A tag is also expressed as a specific co-occurrence term vector. Ultimately, a scheme is devised to determine the similarity between each tag and its target blog post. Then the tags with less similarity value can be identified as poor tag. The experimental results indicate that the proposed method achieves more promising performance than the baselines on datasets collected from Sina Blog, which is one of the biggest Chinese blogs.},
  doi       = {10.1109/FSKD.2015.7382215},
  keywords  = {social networking (online);text analysis;Chinese blogs;LDA topic modeling;Sina Blog;blog post annotation;blog posts;blogger features;blogger modelling;blogger preference representation;co-occurrence term vector;irregular tag quality;latent Dirichlet allocation topic modeling;latent interest topic mining;quantitative analysis;relatively stable semantic relationship;similarity value;tag quality evaluation;tag spam;text contents;topic models;unsupervised approach;user preferences;vocabulary words;Adaptation models;Blogs;Collaboration;Reliability;Resource management;Semantics;Tagging;blog representation;semantic similarity;tag quality evaluation;topic model},
}

@Article{caeiro-rodriguez_etal_2015,
  author   = {M. Caeiro-Rodríguez and J. M. Santos-Gago and M. Lama and M. Llamas-Nistal},
  title    = {A Keyword Recommendation Experiment to Support Information Organization and Folksonomies in Edu-AREA},
  journal  = {IEEE Revista Iberoamericana de Tecnologias del Aprendizaje},
  year     = {2015},
  volume   = {10},
  number   = {2},
  pages    = {60-68},
  month    = {May},
  issn     = {1932-8540},
  abstract = {Edu-AREA is a Web 2.0 application whose main goal is to contribute to teaching innovation. It provides descriptions of educational resources and guidelines that can be used by teachers to create their lesson plans and later to document their teaching experiences. At the current stage of the Edu-AREA development, a main issue is related to the management and classification of information provided by users. This paper introduces a folksonomy approach, the architecture of the system, and the results of an experiment about keyword recommendations.},
  doi      = {10.1109/RITA.2015.2417953},
  keywords = {pattern classification;recommender systems;social networking (online);teaching;Edu-AREA;Web 2.0 application;educational guidelines;educational resources;folksonomy approach;information classification;information management;information organization;keyword recommendation;lesson plan creation;system architecture;teaching experience documentation;teaching innovation;Data models;Education;Ontologies;Organizations;Semantics;Vocabulary;Web 2.0;Educational activities;Information sharing;Social network services;Tagging;educational activities;information sharing;tagging},
}

@Article{lu_etal_2015,
  author   = {Jie Lu and Dianshuang Wu and Mingsong Mao and Wei Wang and Guangquan Zhang},
  title    = {Recommender system application developments: A survey},
  journal  = {Decision Support Systems},
  year     = {2015},
  volume   = {74},
  pages    = {12 - 32},
  issn     = {0167-9236},
  doi      = {http://dx.doi.org/10.1016/j.dss.2015.03.008},
  keywords = {Recommender systems, E-service personalization, E-commerce, E-learning, E-government},
  url      = {http://www.sciencedirect.com/science/article/pii/S0167923615000627},
}

@Article{zou_etal_2015,
  author   = {Benyou Zou and Cuiping Li and Liwen Tan and Hong Chen},
  title    = {GPUTENSOR: Efficient tensor factorization for context-aware recommendations},
  journal  = {Information Sciences},
  year     = {2015},
  volume   = {299},
  pages    = {159 - 177},
  issn     = {0020-0255},
  abstract = {Recommendation systems play an important role in many practical applications that help users manage information and provide personalized recommendations. The context in which a choice is made is an important factor for recommendation systems. Recently, researchers extended the classical matrix factorization to enable generic integration of contextual information by modeling the relevant data as a tensor. However, current tensor factorization methods have two important limitations: (1) computing costs can be very high in practice and (2) they can only be applied to static conditions. In this paper, we propose the GPUTENSOR, a parallel tensor factorization algorithm, to accelerate the tensor factorization of large datasets to support efficient context-aware recommendations. The basic idea of this algorithm is to partition a tensor into smaller blocks and then exploit the inherent parallelism and high memory bandwidth of Graphics Processing Units (GPU) to perform tensor related operations in parallel. Furthermore, based on the observation that contextual information changes dynamically and frequently, we present GPUTENSOR+, an effective incremental method that models the apparent changes of a tensor by adaptively updating its previously factorized components instead of recomputing them on the whole dataset every time data are changed. Experiments indicate that the proposed methods can achieve significant timesavings without a significant loss in accuracy. As an important data mining tool, these methods can be used a potential basis for many other interesting tensor applications, such as clustering, trend detection, social network analysis, and latent concept discovery.},
  doi      = {http://dx.doi.org/10.1016/j.ins.2014.12.004},
  keywords = {Recommendation system, Context-aware, Tensor factorization},
  url      = {http://www.sciencedirect.com/science/article/pii/S0020025514011414},
}

@Article{klasnja-milicevic_etal_2015,
  author          = {Klašnja-Milićević, A. and Ivanović, M. and Nanopoulos, A.},
  title           = {Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions},
  journal         = {Artificial Intelligence Review},
  year            = {2015},
  volume          = {44},
  number          = {4},
  pages           = {571-604},
  note            = {cited By},
  abstract        = {With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities. © 2015, Springer Science+Business Media Dordrecht.},
  affiliation     = {University of Novi Sad, Novi Sad, Serbia; Katholische Universität Eichstätt-Ingolstadt, Ostenstraße 26, Eichstätt, Germany},
  author_keywords = {Collaborative tagging; e-Learning; Personalization; Recommender systems},
  document_type   = {Article},
  doi             = {10.1007/s10462-015-9440-z},
  source          = {Scopus},
  url             = {https://link.springer.com/article/10.1007%2Fs10462-015-9440-z},
}

@PhdThesis{sharif_2009,
  author = {Sharif, Atefeh},
  title  = {Combining ontology and folksonomy: An Integrated Approach to Knowledge Representation},
  year   = {2009},
  url    = {https://core.ac.uk/download/pdf/11887239.pdf},
}

@Article{bouadjenek_etal_2016,
  author   = {Mohamed Reda Bouadjenek and Hakim Hacid and Mokrane Bouzeghoub},
  title    = {Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms},
  journal  = {Information Systems},
  year     = {2016},
  volume   = {56},
  pages    = {1 - 18},
  issn     = {0306-4379},
  doi      = {http://dx.doi.org/10.1016/j.is.2015.07.008},
  keywords = {Information Retrieval, Social networks, Social Information Retrieval, Social search, Social recommendation, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S030643791500160X},
}

@Article{spyrou_mylonas_2016,
  author   = {Evaggelos Spyrou and Phivos Mylonas},
  title    = {A survey on Flickr multimedia research challenges},
  journal  = {Engineering Applications of Artificial Intelligence},
  year     = {2016},
  volume   = {51},
  pages    = {71 - 91},
  issn     = {0952-1976},
  note     = {Mining the Humanities: Technologies and Applications},
  doi      = {http://dx.doi.org/10.1016/j.engappai.2016.01.006},
  keywords = {Flickr, Retrieval, Recommendations, Localization, Travel applications, Knowledge extraction, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S0952197616000105},
}

@Article{tibely_etal_2016,
  author        = {Tibély, G. and Pollner, P. and Palla, G.},
  title         = {Comparing the hierarchy of author given tags and repository given tags in a large document archive},
  journal       = {European Physical Journal: Special Topics},
  year          = {2016},
  volume        = {225},
  number        = {10},
  pages         = {2025-2032},
  note          = {cited By},
  abstract      = {Folksonomies – large databases arising from collaborative tagging of items by independent users - are becoming an increasingly important way of categorizing information. In these systems users can tag items with free words, resulting in a tripartite item-tag-user network. Although there are no prescribed relations between tags, the way users think about the different categories presumably has some built in hierarchy, in which more special concepts are descendants of some more general categories. Several applications would benefit from the knowledge of this hierarchy. Here we apply a recent method to check the differences and similarities of hierarchies resulting from tags given by independent individuals and from tags given by a centrally managed repository system. The results from our method showed substantial differences between the lower part of the hierarchies, and in contrast, a relatively high similarity at the top of the hierarchies. © 2016, EDP Sciences and Springer.},
  affiliation   = {Dept. of Biological Physics, Eötvös University, Budapest, Hungary; MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, Hungary},
  document_type = {Article},
  doi           = {10.1140/epjst/e2015-50154-9},
  source        = {Scopus},
  url           = {https://link.springer.com/article/10.1140%2Fepjst%2Fe2015-50154-9},
}

@Article{godoy_corbellini_2016,
  author        = {Godoy, D. and Corbellini, A.},
  title         = {Folksonomy-Based Recommender Systems: A State-of-the-Art Review},
  journal       = {International Journal of Intelligent Systems},
  year          = {2016},
  volume        = {31},
  number        = {4},
  pages         = {314-346},
  note          = {cited By},
  abstract      = {Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open-ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy-based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link-analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines. © 2015 Wiley Periodicals, Inc.},
  affiliation   = {ISISTAN Research Institute, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional Del Centro de la Provincia de Buenos Aires (UNICEN), University Campus, Paraje Arroyo Seco (BBO7001B), Tandil, Buenos Aires, Argentina},
  document_type = {Review},
  doi           = {10.1002/int.21753},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957435503&doi=10.1002%2fint.21753&partnerID=40&md5=d5cdd0d6da89b95cdfd87fb1b249a531},
}

@Article{sun_etal_2016,
  author        = {Sun, Y. and Sun, H. and Cheng, R.},
  title         = {Fast and semantic measurements on collaborative tagging quality},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2016},
  volume        = {9652 LNAI},
  pages         = {363-375},
  note          = {cited By},
  abstract      = {This paper focuses on the problem of tagging quality evaluation in collaborative tagging systems. By investigating the dynamics of tagging process, we find that high frequency tags almost cover the main aspects of a resource content and can be determined stable much earlier than a whole tag set. Motivated by this finding, we design the swapping index and smart moving index on tagging quality. We also study the correlations in tag usage and propose the semantic measurement on tagging quality. The proposed methods are evaluated against real datasets and the results show that they are more efficient than previous methods, which are appropriate for a large number of web resources. The effectiveness is justified by the results in tag based applications. The light weight metrics bring a little loss on the performance, while the semantic metric is better than current methods. © Springer International Publishing Switzerland 2016.},
  affiliation   = {School of Computer Science and Technology, Shandong University, Jinan, China; Engineering Research Center of Digital Media Technology, MOE, Jinan, China; Software College, Shandong University, Jinan, China; Department of Computer Science, The University of Hong Kong, Hong Kong},
  document_type = {Conference Paper},
  doi           = {10.1007/978-3-319-31750-2_29},
  source        = {Scopus},
  url           = {https://link.springer.com/chapter/10.1007%2F978-3-319-31750-2_29},
}

@Article{yang_wang_2017,
  author   = {J. Yang and J. Wang},
  title    = {Tag clustering algorithm LMMSK: Improved K-means algorithm based on latent semantic analysis},
  journal  = {Journal of Systems Engineering and Electronics},
  year     = {2017},
  volume   = {28},
  number   = {2},
  pages    = {374-384},
  month    = {April},
  abstract = {With the wide application of Web_2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system.},
  doi      = {10.21629/JSEE.2017.02.18},
  keywords = {Algorithm design and analysis;Clustering algorithms;Clustering methods;Data processing;Semantics;Tagging;Web 2.0;K-means;latent semantic analysis (LSA);min-max similarity (MMS);social tagging;tag clustering algorithm},
}

@InProceedings{yang_etal_2017a,
  author    = {L. C. Yang and S. Y. Chou and J. Y. Liu and Y. H. Yang and Y. A. Chen},
  title     = {Revisiting the problem of audio-based hit song prediction using convolutional neural networks},
  booktitle = {2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year      = {2017},
  pages     = {621-625},
  month     = {March},
  abstract  = {Being able to predict whether a song can be a hit has important applications in the music industry. Although it is true that the popularity of a song can be greatly affected by external factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to extend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural network model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio information in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.},
  doi       = {10.1109/ICASSP.2017.7952230},
  keywords  = {audio signal processing;learning (artificial intelligence);music;neural nets;Chinese Pop songs;JYnet model;Western Pop songs;audio features;audio information;audio-based hit song prediction;autotagging;convolutional neural network model;deep learning;deep structures;feature learning;inception model;music industry;prediction models;primitive mel-spectrogram;song dataset;supervised pretraining;Computational modeling;Convolution;Machine learning;Music;Neural networks;Predictive models;Training;Hit song prediction;convolutional neural network;cultural factors;deep learning;music tags},
}

@InBook{klasnja-milicevic_etal_2017,
  pages     = {77--112},
  title     = {Folksonomy and Tag-Based Recommender Systems in E-Learning Environments},
  publisher = {Springer International Publishing},
  year      = {2017},
  author    = {Kla{\v{s}}nja-Mili{\'{c}}evi{\'{c}}, Aleksandra and Vesin, Boban and Ivanovi{\'{c}}, Mirjana and Budimac, Zoran and Jain, Lakhmi C.},
  address   = {Cham},
  isbn      = {978-3-319-41163-7},
  abstract  = {Collaborative tagging is technique, highly employed in different domains, which is used for automatic analysis of users' preferences and recommendations. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing reputation of the collaborative tagging systems, tags could be interesting and provide useful information to enhance algorithms for recommender systems. Besides helping user to organize his/her personal collections, a tag also can be regarded as a user's personal opinion expression, while tagging can be considered as implicit rating or voting on the tagged information resources or items. The overview, presented in this chapter includes descriptions of content-based recommender systems, collaborative filtering systems, hybrid approach, memory-based and model-based algorithms, features of collaborative tagging that are generally attributed to their success and popularity, as well as a model for tagging activities and tag-based recommender systems.},
  booktitle = {E-Learning Systems: Intelligent Techniques for Personalization},
  doi       = {10.1007/978-3-319-41163-7_7},
  url       = {https://doi.org/10.1007/978-3-319-41163-7_7},
}

@Article{kleinberg_1999,
  author     = {Kleinberg, Jon M.},
  title      = {Authoritative Sources in a Hyperlinked Environment},
  journal    = {J. ACM},
  year       = {1999},
  volume     = {46},
  number     = {5},
  pages      = {604--632},
  month      = sep,
  issn       = {0004-5411},
  acmid      = {324140},
  address    = {New York, NY, USA},
  doi        = {10.1145/324133.324140},
  issue_date = {Sept. 1999},
  keywords   = {World Wide Web, graph algorithms, hypertext structure, link analysis},
  numpages   = {29},
  publisher  = {ACM},
  url        = {http://doi.acm.org/10.1145/324133.324140},
}

@Article{kipp_campbell_2006,
  author        = {Kipp, M.E.I. and Campbell, D.G.},
  title         = {Patterns and inconsistencies in collaborative tagging systems: An examination of tagging practices},
  journal       = {Proceedings of the ASIST Annual Meeting},
  year          = {2006},
  volume        = {43},
  note          = {cited By},
  abstract      = {This paper analyzes the tagging patterns exhibited by users of del.icio.us, to assess how collaborative tagging supports and enhances traditional ways of classifying and indexing documents. Using frequency data and co-word analysis matrices analyzed by multi-dimensional scaling, the authors discovered that tagging practices to some extent work in ways that are continuous with conventional indexing. Small numbers of tags tend to emerge by unspoken consensus, and inconsistencies follow several predictable patterns that can easily be anticipated. However, the tags also indicated intriguing practices relating to time and task which suggest the presence of an extra dimension in classification and organization, a dimension which conventional systems are unable to facilitate.},
  affiliation   = {Faculty of Information and Media Studies, University of Western Ontario, Canada},
  document_type = {Conference Paper},
  keywords      = {rank1},
  page_count    = {9},
  source        = {Scopus},
  url           = {http://onlinelibrary.wiley.com/doi/10.1002/meet.14504301178/pdf},
}

@Article{guy_tonkin_2006,
  author  = {Guy, Marieke and Tonkin, Emma},
  title   = {Tidying up tags},
  journal = {D-lib Magazine},
  year    = {2006},
  volume  = {12},
  number  = {1},
  pages   = {1082--9873},
  url     = {http://www.dlib.org/dlib/january06/guy/01guy.html},
}

@Conference{al-khalifa_davis_2007,
  author          = {Al-Khalifa, H.S. and Davis, H.C.},
  title           = {Towards better understanding of folksonomic patterns},
  year            = {2007},
  pages           = {163-166},
  note            = {cited By},
  abstract        = {Folksonomies provide a free source of keywords describing web resources; however, these keywords are free form and their semantics spans multiple contextual dimension. In this paper, we present a pragmatic experiment that analyzes folksonomies using three classification categories: Personal, Factual and Subjective, in order to gain more understanding of the types of tags used in the social tagging process. The rational for this work was to measure the potential portion of folksonomy tags that might be helpful when considering the creation of structured metadata. Copyright 2007 ACM.},
  affiliation     = {Learning Societies Lab., ECS, Southampton University, SO17 1BJ, Southampton, United Kingdom},
  author_keywords = {Collaborative tagging; Folksonomy; Social bookmarking},
  document_type   = {Conference Paper},
  doi             = {10.1145/1286240.1286288},
  journal         = {Hypertext 2007: Proceedings of the Eighteenth ACM Conference on Hypertext and Hypermedia, HT'07},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=1286240.1286288},
}

@Article{ji_etal_2007,
  author        = {Ji, A.-T. and Yeon, C. and Kim, H.-N. and Jo, G.-S.},
  title         = {Collaborative tagging in recommender systems},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2007},
  volume        = {4830 LNAI},
  pages         = {377-386},
  note          = {cited By},
  abstract      = {This paper proposes a collaborative filtering method with usercreated tags focusing on changes of web content and internet services. 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 future searching and information sharing which is used for automatic analysis of user preference and recommendation. We present empirical experiments using real dataset from del.icio.us to demonstrate our algorithm and evaluate performance compared with existing works. © Springer-Verlag Berlin Heidelberg 2007.},
  affiliation   = {Intelligent E-Commerce Systems Laboratory, Inha University, South Korea},
  document_type = {Conference Paper},
  keywords      = {rank1},
  source        = {Scopus},
  url           = {https://link.springer.com/chapter/10.1007/978-3-540-76928-6_39},
}

@InProceedings{turnbull_etal_2008,
  author    = {Douglas Turnbull and Luke Barrington and Gert Lanckriet},
  title     = {Five approaches to collecting tags for music},
  booktitle = {In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR},
  year      = {2008},
  url       = {https://www.cs.swarthmore.edu/~turnbull/Papers/Turnbull_FiveTag_ISMIR08.pdf},
}

@Conference{chen_etal_2009,
  author          = {Chen, L. and Wright, P. and Nejdl, W.},
  title           = {Improving music genre classification using collaborative tagging data},
  year            = {2009},
  pages           = {84-93},
  note            = {cited By},
  abstract        = {As a fundamental and critical component of music information retrieval (MIR) systems, music genre classification has attracted considerable research attention. Automatically classifying music by genre is, however, a challenging problem due to the fact that music is an evolving art. While most of the existing work categorizes music using features extracted from music audio signals, in this paper, we propose to exploit the semantic information embedded in tags supplied by users of social networking websites. Particularly, we consider the tag information by creating a graph of tracks so that tracks are neighbors if they are similar in terms of their associated tags. Two classification methods based on the track graph are developed. The first one employs a classification scheme which simultaneously considers the audio content and neighborhood of tracks. In contrast, the second one is a two-level classifier which initializes genre label for unknown tracks using their audio content, and then iteratively updates the genres considering the influence from their neighbors. A set of optimizing strategies are designed for the purpose of further enhancing the quality of the two-level classifier. Extensive experiments are conducted on real-world data collected from Last.fm. Promising experimental results demonstrate the benefit of using tags for accurate music genre classification. Copyright 2009 ACM.},
  affiliation     = {L3S Research Center, Leibniz University, Hannover, Germany; College of Computing, Georgia Institute of Technology, United States},
  author_keywords = {Exploiting tag semantics; Music genre classification; Relaxation labelling},
  document_type   = {Conference Paper},
  doi             = {10.1145/1498759.1498812},
  journal         = {Proceedings of the 2nd ACM International Conference on Web Search and Data Mining, WSDM'09},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349129938&doi=10.1145%2f1498759.1498812&partnerID=40&md5=077ebcf735ba081473841ee54a0bad72},
}

@Article{memmel_etal_2009,
  author          = {Memmel, M. and Kockler, M. and Schirru, R.},
  title           = {Providing multi source tag recommendations in a social resource sharing platform},
  journal         = {Journal of Universal Computer Science},
  year            = {2009},
  volume          = {15},
  number          = {3},
  pages           = {678-691},
  note            = {cited By},
  abstract        = {In today's information environments, tagging is widely used to provide information about arbitrary types of digital resources. This information is usually created by end users with different motivations and for different kinds of purposes. When aiming to support users in the tagging process, these differences play an important role. In this paper several approaches to generate tag recommendations are discussed, and a prototypical recommender system for the social resource sharing platform ALOE is presented. This interactive system allows users to control the generation of the recommendations by selecting the sources to be used as well as their impact. The component was introduced at DFKI, and a first evaluation showed that the recommender component was considered as helpful by a majority of users. © J.UCS.},
  affiliation     = {Knowledge Management Department DFKI GmbH, Kaiserslautern, Germany},
  author_keywords = {Classification; Collaborative tagging; Digital resources; Knowledge management; Knowledge sharing; Metadata; Recommender; Tagging; Web 2.0},
  document_type   = {Article},
  keywords        = {rank2},
  source          = {Scopus},
  url             = {http://www.jucs.org/jucs_15_3/providing_multi_source_tag/jucs_15_03_0678_0691_memmel.pdf},
}

@InProceedings{kungas_dumas_2009,
  author    = {P. Kungas and M. Dumas},
  title     = {Cost-Effective Semantic Annotation of XML Schemas and Web Service Interfaces},
  booktitle = {2009 IEEE International Conference on Services Computing},
  year      = {2009},
  pages     = {372-379},
  month     = {Sept},
  abstract  = {Research in the field of semantic Web services aims at automating the discovery, selection, composition and management of Web services based on semantic descriptions. However, the applicability of many solutions developed in this field is hampered by the costs associated with semantically annotating large repositories of Web services. To overcome this gap we propose a practical method for semantically annotating collections of XML Schemas and Web service interfaces. We have evaluated this method on a large repository of governmental Web services. The evaluation shows that relatively simple techniques are surprisingly cost-effective, saving hundreds of man-hours of semantic annotation effort. Moreover, the proposed method does not assume the availability of a preexisting ontology or controlled vocabulary. Instead, the space of annotations is dynamically built during the annotation process.},
  doi       = {10.1109/SCC.2009.17},
  keywords  = {Web services;XML;semantic Web;Web service interfaces;XML schemas;cost-effective semantic annotation;semantic Web services;Application software;Assembly;Humans;Middleware;Natural language processing;Natural languages;System testing;TV;Web services;XML;Web Services interfaces;semantic annotation},
}

@InProceedings{nakashima_etal_2009,
  author    = {M. Nakashima and T. Hiyama and K. Sato and T. Ito},
  title     = {Proceeding with Keyword-Based Web-Image Annotation Conceptually in Folksonomy},
  booktitle = {2009 International Conference on Complex, Intelligent and Software Intensive Systems},
  year      = {2009},
  pages     = {995-1000},
  month     = {March},
  abstract  = {Image annotation becomes increasingly more important as the Web continues to grow. We propose a novel approach to enhancing keyword-based Web-image annotation in folksonomy, where a volunteer user is notified what kind(s) of keywords are necessary, and that keywords have been sufficiently provided by other volunteer users. Necessary information is obtained from the keyword-filled facet tables, and sufficient information provided by the quantified conceptual keyword relations. Though the facet tables and the conceptual keyword relations are set by referring to an ontology, users do not need to know its details to successfully annotate the images.},
  doi       = {10.1109/CISIS.2009.115},
  keywords  = {Internet;image retrieval;ontologies (artificial intelligence);folksonomy system;image search;keyword-based Web-image annotation;keyword-filled facet table;ontology;quantified conceptual keyword relation;Competitive intelligence;Computer science;Facial animation;History;Image retrieval;Indium tin oxide;Intelligent systems;Ontologies;Software systems;Vocabulary;based image annotation;conceptual keyword relations;facet tables;folksonomy;ontology},
}

@InProceedings{antonellis_etal_2009,
  author    = {Ioannis Antonellis and Hector Garcia-Molina and Jawed Karim},
  title     = {Tagging with Queries: How and Why?},
  booktitle = {Second ACM International Conference on Web Search and Data Mining WSDM 2009, Late Breaking Results Session},
  year      = {2009},
  month     = {February},
  publisher = {Infolab},
  abstract  = {Web search queries capture the information need of search engine users. Search engines store these queries in their logs and analyze them to guide their search results.

In this work, we argue that not only a search engine can benefit from data stored in these logs, but also the web users. We first show how clickthrough logs can be collected in a distributed fashion using the http referer field in web server access logs. We then perform a set of experiments to study the information value of search engine queries when treated as "tags" or "labels" for the web pages that both appear as a result and the user actually clicks on. We ask how much extra information these query
tags provide for web pages by comparing them to tags from the del.icio.us bookmarking site and to the pagetext. We find that query tags can provide substantially many (on average 250 tags per URL), new tags (on average 125 tags per URL are not present in the pagetext) for a large fraction of the Web.},
  keywords  = {query tags, query logs, click logs, tagging, web search, web navigation, rank1},
  url       = {http://ilpubs.stanford.edu:8090/903/},
}

@InProceedings{musto_etal_2009,
  author    = {Musto, Cataldo and Narducci, Fedelucio and De Gemmis, Marco and Lops, Pasquale and Semeraro, Giovanni},
  title     = {STaR: A Social Tag Recommender System},
  booktitle = {Proceedings of the 2009th International Conference on ECML PKDD Discovery Challenge - Volume 497},
  year      = {2009},
  series    = {ECMLPKDDDC'09},
  pages     = {215--227},
  address   = {Aachen, Germany, Germany},
  publisher = {CEUR-WS.org},
  acmid     = {3056164},
  keywords  = {collaborative tagging systems, folksonomies, recommender systems, web 2.0},
  location  = {Bled, Slovenia},
  numpages  = {13},
  url       = {http://dl.acm.org/citation.cfm?id=3056147.3056164},
}

@InProceedings{panagakis_kotropoulos_2011,
  author    = {Y. Panagakis and C. Kotropoulos},
  title     = {Automatic music mood classification via Low-Rank Representation},
  booktitle = {2011 19th European Signal Processing Conference},
  year      = {2011},
  pages     = {689-693},
  month     = {Aug},
  abstract  = {The problem of automatic music mood classification is addressed by resorting to low-rank representation of slow auditory spectro-temporal modulations. Recently, it has been shown that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless, the lowest-rank representation (LRR) of a set of test vector samples with respect to a set of training vector samples has the nature of being both dense for within-class affinities and almost zero for between-class affinities. Consequently, the LRR exactly reveals the classification of the data, resulting into the so-called Low-Rank Representation-based Classification (LRRC). The performance of the LRRC is compared against three well-known classifiers, namely the Sparse Representations-based Classifier, Support Vector Machines, and Nearest Neighbor classifiers for music mood classification by conducting experiments on the MTV and the Soundtracks180 datasets. The experimental results validate the effectiveness of the LRRC among the classifiers that is compared to.},
  issn      = {2076-1465},
  keywords  = {music;pattern classification;support vector machines;LRRC;MTV;Soundtracks180 datasets;automatic music mood classification;low-rank representation-based classification;nearest neighbor classifiers;slow auditory spectro-temporal modulations;sparse representations-based classifier;support vector machines;Accuracy;Modulation;Mood;Psychoacoustic models;Support vector machines;Training;Vectors},
  url       = {https://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569427041.pdf},
}

@Conference{ilhan_oegueduecue_2011,
  author          = {Ilhan, N. and Ögüdücü, Ş.G.},
  title           = {Tag recommendation based on user's behavior in collaborative tagging systems},
  year            = {2011},
  volume          = {1},
  pages           = {570-573},
  note            = {cited By},
  abstract        = {Social bookmarking Web sites allow users submitting their resources and labeling them with arbitrary keywords, called tags, to create folksonomies. Tag recommendation is an important element of collaborative tagging systems which aims at providing relevant information to users by proposing a set of tags to each newly posted resource. In this paper, we focus on the task of tag recommendation when a user examines a document based on the user's tagging behavior. We explore the use of this semantic relationship in modeling the user tagging behavior. The experiments are performed on the data set obtained from a social bookmarking site. Our experimental result show that our method is efficient in modeling users' tagging behavior and it can be used to recommend tags for resources.},
  affiliation     = {Istanbul Technical University, Department of Computer Engineering, Maslak, Istanbul, 34469, Turkey},
  author_keywords = {Collaborative tagging; Recommender systems; Social network analysis; Tag suggestions},
  document_type   = {Conference Paper},
  journal         = {ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960109961&partnerID=40&md5=c25b8bea2544da0c19f2d6ab82aa0a2f},
}

@InProceedings{belem_etal_2012,
  author    = {Bel{\'e}m, Fabiano Muniz and Martins, Eder Ferreira and Almeida, Jussara Marques and Gon\c{c}alves, Marcos Andr{\'e}},
  title     = {Exploiting Relevance, Novelty and Diversity in Tag Recommendation},
  booktitle = {Proceedings of the 18th Brazilian Symposium on Multimedia and the Web},
  year      = {2012},
  series    = {WebMedia '12},
  pages     = {297--300},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {2382699},
  doi       = {10.1145/2382636.2382699},
  isbn      = {978-1-4503-1706-1},
  keywords  = {diversidade, novidade, recomenda??o de tags},
  location  = {S\&\#227;o Paulo/SP, Brazil},
  numpages  = {4},
  url       = {http://doi.acm.org/10.1145/2382636.2382699},
}

@InProceedings{oliveira_etal_2012,
  author    = {Oliveira, Vitor and Gomes, Guilherme and Bel{\'e}m, Fabiano and Brand\~{a}o, Wladmir and Almeida, Jussara and Ziviani, Nivio and Gon\c{c}alves, Marcos},
  title     = {Automatic Query Expansion Based on Tag Recommendation},
  booktitle = {Proceedings of the 21st ACM International Conference on Information and Knowledge Management},
  year      = {2012},
  series    = {CIKM '12},
  pages     = {1985--1989},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {2398557},
  doi       = {10.1145/2396761.2398557},
  isbn      = {978-1-4503-1156-4},
  keywords  = {query expansion, tag recommendation, wikipedia},
  location  = {Maui, Hawaii, USA},
  numpages  = {5},
  url       = {http://doi.acm.org/10.1145/2396761.2398557},
}

@Article{lu_etal_2013,
  author   = {W. Lu and J. Li and T. Li and W. Guo and H. Zhang and J. Guo},
  title    = {Web Multimedia Object Classification Using Cross-Domain Correlation Knowledge},
  journal  = {IEEE Transactions on Multimedia},
  year     = {2013},
  volume   = {15},
  number   = {8},
  pages    = {1920-1929},
  month    = {Dec},
  issn     = {1520-9210},
  abstract = {Given a collection of web images with the corresponding textual descriptions, in this paper, we propose a novel cross-domain learning method to classify these web multimedia objects by transferring the correlation knowledge among different information sources. Here, the knowledge is extracted from unlabeled objects through unsupervised learning and applied to perform supervised classification tasks. To mine more meaningful correlation knowledge, instead of using commonly used visual words in the traditional bag-of-visual-words (BoW) model, we discover higher level visual components (words and phrases) to incorporate the spatial and semantic information into our image representation model, i.e., bag-of-visual-phrases (BoP). By combining the enriched visual components with the textual words, we calculate the frequently co-occurring pairs among them to construct a cross-domain correlated graph in which the correlation knowledge is mined. After that, we investigate two different strategies to apply such knowledge to enrich the feature space where the supervised classification is performed. By transferring such knowledge, our cross-domain transfer learning method can not only handle large scale web multimedia objects, but also deal with the situation that the textual descriptions of a small portion of web images are missing. Empirical experiments on two different datasets of web multimedia objects are conducted to demonstrate the efficacy and effectiveness of our proposed cross-domain transfer learning method.},
  doi      = {10.1109/TMM.2013.2280895},
  keywords = {Internet;correlation methods;graph theory;image classification;image representation;knowledge acquisition;multimedia computing;text analysis;unsupervised learning;BoP;BoW;Web image collection;Web multimedia object classification;bag-of-visual-phrases;bag-of-visual-words model;cross-domain correlated graph;cross-domain correlation knowledge;cross-domain transfer learning method;feature space;image representation model;information sources;knowledge extraction;supervised classification tasks;textual descriptions;unsupervised learning;visual components;Correlation;Feature extraction;Knowledge engineering;Learning systems;Multimedia communication;Semantics;Visualization;Bag-of-Visual-Phrases Model;Correlation Knowledge;Cross-Domain;Multimedia Object Classification;Transfer Learning},
}

@InProceedings{wang_etal_2013a,
  author    = {T. Wang and H. Wang and G. Yin and C. X. Ling and X. Li and P. Zou},
  title     = {Mining Software Profile across Multiple Repositories for Hierarchical Categorization},
  booktitle = {2013 IEEE International Conference on Software Maintenance},
  year      = {2013},
  pages     = {240-249},
  month     = {Sept},
  abstract  = {The large amounts of software repositories over the Internet are fundamentally changing the traditional paradigms of software maintenance. Efficient categorization of the massive projects for retrieving the relevant software in these repositories is of vital importance for Internet-based maintenance tasks such as solution searching, best practices learning and so on. Many previous works have been conducted on software categorization by mining source code or byte code, which are only verified on relatively small collections of projects with coarse-grained categories or clusters. However, Internet-based software maintenance requires finer-grained, more scalable and language-independent categorization approaches. In this paper, we propose a novel approach to hierarchically categorize software projects based on their online profiles across multiple repositories. We design a SVM-based categorization framework to classify the massive number of software hierarchically. To improve the categorization performance, we aggregate different types of profile attributes from multiple repositories and design a weighted combination strategy which assigns greater weights to more important attributes. Extensive experiments are carried out on more than 18,000 projects across three repositories. The results show that our approach achieves significant improvements by using weighted combination, and the overall precision, recall and F-Measure can reach 71.41%, 65.60% and 68.38% in appropriate settings. Compared to the previous work, our approach presents competitive results with 123 finer-grained and multi-layered categories. In contrast to those using source code or byte code, our approach is more effective for large-scale and language-independent software categorization.},
  doi       = {10.1109/ICSM.2013.35},
  issn      = {1063-6773},
  keywords  = {Internet;data mining;pattern classification;software maintenance;software management;support vector machines;F-Measure;Internet-based software maintenance;SVM-based categorization;byte code;hierarchical categorization;language-independent software categorization;large-scale software categorization;online profiles;recall;software hierarchical classification;software profile mining;software projects;software repositories;source code;support vector machines;weighted combination strategy;Collaboration;Data mining;Databases;Internet;Servers;Software maintenance;Hierarchical Categorization;Software Profile;Software Repository},
}

@InProceedings{jung_etal_2013,
  author    = {Y. Jung and Y. Cho and Y. M. Park and T. Lee},
  title     = {Automatic Tagging of Functional-Goals for Goal-Driven Semantic Service Discovery},
  booktitle = {2013 IEEE Seventh International Conference on Semantic Computing},
  year      = {2013},
  pages     = {212-219},
  month     = {Sept},
  abstract  = {With the growing number of Web services available on the Web, finding services that match user's query becomes more difficult. To deal with the problem, numerous approaches employed semantic techniques in terms of annotation (or tagging) and service discovery algorithms. However, the problem of semantic service discovery based on the identification of fine-grained goals of users and services is still challenging due to the lack of semantic information in Web services. To enable the goal-driven semantic service discovery, we propose an automatic functional-goals tagging approach which employs a set of natural language processing (NLP) procedures based on the descriptions of Web services. In addition, to check the effectiveness of the functional-goals tagged, we designed a goal-driven semantic service discovery algorithm and compared it with other approaches: keyword-based, ontology-based, and topic-based service discovery. As results, our proposed goal-driven semantic service discovery achieved 75.7% of precision and 59.6% of recall (finally, F1=62%) that outperform other discovery approaches.},
  doi       = {10.1109/ICSC.2013.45},
  keywords  = {Web services;natural language processing;ontologies (artificial intelligence);query processing;NLP;Web services;annotation;automatic functional-goals tagging approach;goal-driven semantic service discovery algorithm;keyword-based service discovery;natural language processing;ontology-based service discovery;semantic information;semantic techniques;topic-based service discovery;user query;Data mining;Google;Ontologies;Semantics;Tagging;Videos;Web services;automatic tagging;functional-goal;goal-driven semantic service discovery;web service},
}

@Article{xu_etal_2014a,
  author        = {Zheng Xu and Yunhuai Liu and Lin Mei and Chuanping Hu and Lan Chen},
  title         = {Generating temporal semantic context of concepts using web search engines},
  journal       = {Journal of Network and Computer Applications},
  year          = {2014},
  volume        = {43},
  pages         = {42 - 55},
  issn          = {1084-8045},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.jnca.2014.04.002},
  keywords      = {Temporal semantic context, Semantic annotation, Content analysis, Web mining},
  url           = {http://www.sciencedirect.com/science/article/pii/S108480451400085X},
}

@Conference{gueye_etal_2014,
  author        = {Gueye, M. and Abdessalem, T. and Naacke, H.},
  title         = {A parameter-free algorithm for an optimized tag recommendation list size},
  year          = {2014},
  pages         = {233-240},
  note          = {cited By},
  abstract      = {Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend suitable tags to a user for tagging an item. One of its main challenges is the effectiveness of its recommendations. Existing works focus on techniques for retrieving the most relevant tags to give beforehand, with a fixed number of tags in each recommended list. In this paper, we try to optimize the number of recommended tags in order to improve the efficiency of the recommendations. We propose a parameter-free algorithm for determining the optimal size of the recommended list. Thus we introduced some relevance measures to find the most relevant sublist from a given list of recommended tags. More precisely, we improve the quality of our recommendations by discarding some unsuitable tags and thus adjusting the list size. Our solution is an add-on one, which can be implemented on top of many kinds of tag recommenders. The experiments we did on five datasets, using four categories of tag recommenders, demonstrate the efficiency of our technique. For instance, the algorithm we propose outperforms the results of the task 2 of the ECML PKDD Discovery Challenge 2009 1. By using the same tag recommender than the winners of the contest, we reach a F1 measure of 0.366 while the latter got 0.356. Thus, our solution yields significant improvements on the lists obtained from the tag recommenders. Copyright © 2014 ACM.},
  affiliation   = {Université Cheikh Anta Diop, BP. Fann Dakar, Senegal; Institut Telecom - Telecom, ParisTech, 46, rue Barrault 75013, Paris, France; LIP6 - UPMC Sorbonne Universités, 4 place Jussieu 75005, Paris, France},
  document_type = {Conference Paper},
  doi           = {10.1145/2645710.2645727},
  journal       = {RecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908884108&doi=10.1145%2f2645710.2645727&partnerID=40&md5=612d66bd2afa9326ef0395247a44d1dc},
}

@Article{duong_etal_2015,
  author        = {Trong Hai Duong and Ngoc Thanh Nguyen and Hai Bang Truong and Van Huan Nguyen},
  title         = {A collaborative algorithm for semantic video annotation using a consensus-based social network analysis},
  journal       = {Expert Systems with Applications},
  year          = {2015},
  volume        = {42},
  number        = {1},
  pages         = {246 - 258},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2014.07.046},
  keywords      = {Video annotation, Semantic web, Social network, Consensus},
  url           = {http://www.sciencedirect.com/science/article/pii/S0957417414004539},
}

@Article{khan_etal_2017,
  author        = {Umer Khan and Lars Schmidt-Thieme and Alexandros Nanopoulos},
  title         = {Collaborative SVM classification in scale-free peer-to-peer networks},
  journal       = {Expert Systems with Applications},
  year          = {2017},
  volume        = {69},
  pages         = {74 - 86},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2016.10.008},
  keywords      = {Distributed, Classification, SVM, P2P, Skew},
  url           = {http://www.sciencedirect.com/science/article/pii/S0957417416305395},
}

@InProceedings{wang_blei_2011,
  author    = {Wang, Chong and Blei, David M.},
  title     = {Collaborative Topic Modeling for Recommending Scientific Articles},
  booktitle = {Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year      = {2011},
  series    = {KDD '11},
  pages     = {448--456},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {2020480},
  doi       = {10.1145/2020408.2020480},
  isbn      = {978-1-4503-0813-7},
  keywords  = {collaborative filtering, latent structure interpretation, scientific article recommendation, topic modeling},
  location  = {San Diego, California, USA},
  numpages  = {9},
  url       = {http://doi.acm.org/10.1145/2020408.2020480},
}

@InProceedings{shen_fan_2010,
  author    = {Shen, Yi and Fan, Jianping},
  title     = {Leveraging Loosely-tagged Images and Inter-object Correlations for Tag Recommendation},
  booktitle = {Proceedings of the 18th ACM International Conference on Multimedia},
  year      = {2010},
  series    = {MM '10},
  pages     = {5--14},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1873956},
  doi       = {10.1145/1873951.1873956},
  isbn      = {978-1-60558-933-6},
  keywords  = {loosely-tagged images, multi-task structured svm, multiple instance learning, object correlation network},
  location  = {Firenze, Italy},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/1873951.1873956},
}

@Comment{jabref-meta: databaseType:bibtex;}

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bib