{"url":"/dataset/genre2movies","name":"Genre2Movies","full_name":"Compositional queries for Movie recommendation","description_markdown":"Genre annotations for movies\r\nThe file genre2movies.csv contains genre-movie tuples based on Wikidata annotations (https://www.wikidata.org/).\r\n\r\nData\r\nEach line in genre2movies.csv represents one genre-movie tuple.\r\nThe first entry is the genre.\r\nThe second entry of each line is the movie name.\r\nThere are 83,670 genre-movie tuples.\r\nJoining with the Movielens 20M dataset\r\n\r\nThe movies considered are from the Movielens 20M corpus: https://grouplens.org/datasets/movielens/20m/\r\nThe movie names in genre2movies.csv match the movie 'titles' in Movielens 20M.\r\n\r\nCompositions\r\nThe directory \"compositions\" contains movies assigned to compositions of genres. The compositions are of the form: \"genre A and genre B\", \"genre A and not genre B\", \"genre A and genre B and genre C\", \"genre A and genre B and not genre C\". These assignments have been automatically generated from genre2movies.csv. We try to generate genre-compositions that are useful, e.g., for a \"genre A and genre B\" composition we ensure that genre B is not a subgenre of genre A, because an interesection of a superset with a subset is identical to the subset and does not form a new concept.","description_withheld":null,"homepage":"https://github.com/google-research-datasets/genre2movies","introduced_date":"2023-06-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/answering-compositional-queries-with-set","title":"Answering Compositional Queries with Set-Theoretic Embeddings","first_author":"Shib Dasgupta","url":null},"license":{"name":"Creative Commons Attribution 4.0 International License.","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"},{"name":"Ranking","url":"/datasets/modality/ranking"},{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Complex Query Answering","url":"/task/complex-query-answering","datasets_with_task":"/datasets/task/complex-query-answering"},{"name":"Collaborative Filtering","url":"/task/collaborative-filtering","datasets_with_task":"/datasets/task/collaborative-filtering"},{"name":"Movie Recommendation","url":"/task/movie-recommendation","datasets_with_task":"/datasets/task/movie-recommendation"}],"languages":[],"variants":["Genre2Movies"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}