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README.md
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- name: verified_purchase
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dtype: bool
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- name: review_title
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dtype: string
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- name: review_text
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dtype: string
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- name: date
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dtype: timestamp[ns]
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splits:
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- name: train
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num_bytes: 2299352
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num_examples: 7753
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download_size: 1502832
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dataset_size: 2299352
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- config_name: interactions
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features:
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- name: user_id
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dtype: string
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- name: parent_asin
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dtype: string
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- name: rating
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dtype: float64
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- name: timestamp
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dtype: int64
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- name: helpful_vote
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dtype: int64
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- name: verified_purchase
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dtype: bool
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- name: review_title
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dtype: string
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- name: review_text
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dtype: string
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- name: date
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dtype: timestamp[ns]
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splits:
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- name: train
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num_bytes: 184914008
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num_examples: 473647
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download_size: 105275880
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dataset_size: 184914008
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- config_name: items
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features:
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- name: parent_asin
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dtype: string
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- name: title
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dtype: string
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- name: description
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dtype: string
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- name: features
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dtype: string
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- name: main_category
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dtype: string
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- name: categories
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dtype: string
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- name: store
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dtype: string
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- name: price
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dtype: float64
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- name: average_rating
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dtype: float64
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- name: rating_number
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dtype: int64
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- name: image_url
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dtype: string
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- name: has_image
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dtype: bool
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- name: has_description
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dtype: bool
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- name: has_price
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dtype: bool
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splits:
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- name: train
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num_bytes: 31153350
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num_examples: 23158
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download_size: 17103989
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dataset_size: 31153350
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configs:
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- config_name:
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---
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---
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license: mit
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task_categories:
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- text-classification
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- text-retrieval
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language:
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- en
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tags:
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- recommendation-system
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- collaborative-filtering
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- amazon-reviews
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- musical-instruments
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size_categories:
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- 100K<n<1M
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configs:
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- config_name: interactions
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data_files:
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- split: train
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path: interactions/train-*
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- config_name: items
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data_files:
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- split: train
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path: items/train-*
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- config_name: cold_start
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data_files:
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- split: train
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path: cold_start/train-*
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---
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# Amazon Musical Instruments — 5-Core Sampled Dataset (2018-2023)
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Sampled subset of [Amazon Reviews 2023](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023) Musical Instruments category, created for a Data Mining course recommendation system project.
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## Sampling Strategy
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1. **Time filter:** 2014-01-01 to 2023-09-12
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2. **Iterative 5-core filtering:** Both users and items have at least 5 interactions (iteratively until stable)
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3. **Cold-start test set:** 10000 users with 1-2 reviews held out for cold-start evaluation
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4. **Random seed:** 42
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## Configs
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This dataset has **three configs**. Load each one separately:
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```python
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from datasets import load_dataset
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interactions = load_dataset("oyku-tugana/amazon-musical-instruments-2014-2023-5core", "interactions", split="train")
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items = load_dataset("oyku-tugana/amazon-musical-instruments-2014-2023-5core", "items", split="train")
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cold_start = load_dataset("oyku-tugana/amazon-musical-instruments-2014-2023-5core", "cold_start", split="train")
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```
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## Statistics
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| Field | Value |
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|---|---|
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| Users (main) | 53,127 |
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| Items (main) | 23,158 |
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| Interactions (main) | 473,647 |
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| Sparsity | 0.999615 |
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| Mean rating | 4.445 |
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| Items w/ image | 100.0% |
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| Items w/ description| 69.3% |
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| Items w/ price | 69.2% |
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| Cold-start users | 6,904 |
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## Schema
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### interactions / cold_start
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- `user_id`, `parent_asin`, `rating`, `timestamp` (ms), `date`
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- `helpful_vote`, `verified_purchase`
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- `review_title`, `review_text`
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### items
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- `parent_asin`, `title`, `description`, `features`
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- `main_category`, `categories`, `store`
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- `price` (nullable), `average_rating`, `rating_number`
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- `image_url` (nullable), `has_image`, `has_description`, `has_price`
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