{"url":"/dataset/netflix-prize","name":"Netflix Prize","full_name":"Netflix Prize","description_markdown":"**Netflix Prize** consists of about 100,000,000 ratings for 17,770 movies given by 480,189 users. Each rating in the training dataset consists of four entries: user, movie, date of grade, grade. Users and movies are represented with integer IDs, while ratings range from 1 to 5.\r\n\r\nSource: [The Netflix Prize](https://www.cs.uic.edu/~liub/KDD-cup-2007/proceedings/The-Netflix-Prize-Bennett.pdf)\r\nImage Source: [https://www.netflixprize.com/](https://www.netflixprize.com/)","description_withheld":null,"homepage":"https://www.netflixprize.com/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"The netflix prize","first_author":null,"url":"http://brettb.net/project/papers/2007%20The%20Netflix%20Prize.pdf"},"license":{"name":"Custom","url":"https://www.netflixprize.com/faq.html"},"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Recommendation Systems","url":"/task/recommendation-systems","datasets_with_task":"/datasets/task/recommendation-systems"},{"name":"Fairness","url":"/task/fairness","datasets_with_task":"/datasets/task/fairness"},{"name":"Matrix Completion","url":"/task/matrix-completion","datasets_with_task":"/datasets/task/matrix-completion"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["Netflix","Netflix Prize"],"data_loaders":[],"num_papers_in_archive":370,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/collaborative-filtering-on-netflix","task":"Recommendation Systems","dataset_variant":"Netflix","rows":10,"metrics":["nDCG@100","nDCG@10","Recall@20","Recall@50","AUC","PSP@10","Recall@10","mAP@10","Recall@100"],"first_row_in_archive_order":{"model":"H+Vamp Gated","paper":"/paper/enhancing-vaes-for-collaborative-filtering","metrics":{"Recall@20":"0.37678","Recall@50":"0.46252","nDCG@100":"0.40861"},"code_links":[{"title":"psywaves/EVCF","url":"https://github.com/psywaves/EVCF"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/infinite-recommendation-networks-a-data","title":"Infinite Recommendation Networks: A Data-Centric Approach","date":"2022-06-03","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":13,"samples_ran":8,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/recvae-a-new-variational-autoencoder-for-top","title":"RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback","date":"2019-12-24","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/enhancing-vaes-for-collaborative-filtering","title":"Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms","date":"2019-11-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-amortized-ranking-critical-training","title":"Towards Amortized Ranking-Critical Training for Collaborative Filtering","date":"2019-06-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/190503375","title":"Embarrassingly Shallow Autoencoders for Sparse Data","date":"2019-05-08","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/collaborative-similarity-embedding-for","title":"Collaborative Similarity Embedding for Recommender Systems","date":"2019-02-17","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/variational-autoencoders-for-collaborative","title":"Variational Autoencoders for Collaborative Filtering","date":"2018-02-16","rows_on_this_dataset":2,"code_links":18,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":21,"samples_ran":2,"samples_unverified":19,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/latent-relational-metric-learning-via-memory","title":"Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking","date":"2017-07-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/collaborative-metric-learning","title":"Collaborative Metric Learning","date":"2017-04-01","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":4,"samples_harvested":44,"samples_ran":19,"samples_unverified":25,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}