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--- |
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preview_file_name: ratings.csv |
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license: cc-by-4.0 |
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pretty_name: User Animelist Dataset |
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size_categories: |
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- 1M<n<10M |
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modalities: |
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- tabular |
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task_categories: |
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- tabular-regression |
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- tabular-classification |
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language: |
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- en |
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tags: |
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- Anime |
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- Recommendation |
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- Tabular |
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- Dataset |
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- Recommender |
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- MovieLens |
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--- |
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# About Dataset |
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**This dataset consists of user ratings for anime titles. Each user in the dataset has provided at least 5 ratings, |
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ensuring a minimum level of engagement. The dataset includes detailed information about both users and anime, making it |
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suitable for tasks such as recommendation systems, user behavior analysis, and genre-based filtering. |
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Dataset is freshly-created so it cover newer animes. Data is published in MovieLens format except timestamp data so |
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this dataset is easy to use with GitHub that trains with MovieLens dataset** |
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## Dataset Statistics |
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- Number of Users: 1,774,522 |
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- Number of Animes: 20,237 |
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- Total Ratings: 148,170,496 |
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- Sparsity/Density: 0.0041 |
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- Average Ratings per User: ~83.50 |
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- Average Ratings per Anime: ~7,321.76 |
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- Rating Range: 0.1 to 10.0 |
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- Mean Rating: 7.64 |
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- Standard Deviation of Ratings: 1.89 |
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## Anime Metadata |
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### Each anime entry includes: |
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- Title |
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- Year of release |
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- Episode Count |
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- Type (e.g., TV, Movie, OVA) |
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- Score (aggregated or average rating) |
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- Image URL (for visual reference) |
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- MyAnimeList URL |
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- Genres Detailed Genres |
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## Usage |
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```bash |
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file_path = 'ratings.npy' |
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# ratings_array shape: (n_ratings, 3) - columns: [user_id, anime_id, rating] |
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ratings_array = np.load(file_path) |
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# Create DataFrame from numpy array |
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df = pd.DataFrame(ratings_array, columns=['user_id', 'anime_id', 'rating']) |
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``` |
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## Links |
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Dataset GitHub repo: https://github.com/MRamazan/User-Animelist-Dataset |
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Dataset Kaggle link: https://www.kaggle.com/datasets/tavuksuzdurum/user-animelist-dataset |
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BERT Anime Recommender GitHub repo: https://github.com/MRamazan/AnimeRecBERT |
