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