{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scalable-realistic-recommendation-datasets","title":"Scalable Realistic Recommendation Datasets through Fractal Expansions","arxiv_id":"1901.08910","date":"2019-01-23","proceeding":null,"authors":["Francois Belletti","Karthik Lakshmanan","Walid Krichene","Yi-fan Chen","John Anderson"],"abstract":"Recommender System research suffers currently from a disconnect between the\nsize of academic data sets and the scale of industrial production systems. In\norder to bridge that gap we propose to generate more massive user/item\ninteraction data sets by expanding pre-existing public data sets. User/item\nincidence matrices record interactions between users and items on a given\nplatform as a large sparse matrix whose rows correspond to users and whose\ncolumns correspond to items. Our technique expands such matrices to larger\nnumbers of rows (users), columns (items) and non zero values (interactions)\nwhile preserving key higher order statistical properties. We adapt the\nKronecker Graph Theory to user/item incidence matrices and show that the\ncorresponding fractal expansions preserve the fat-tailed distributions of user\nengagements, item popularity and singular value spectra of user/item\ninteraction matrices. Preserving such properties is key to building large\nrealistic synthetic data sets which in turn can be employed reliably to\nbenchmark Recommender Systems and the systems employed to train them. We\nprovide algorithms to produce such expansions and apply them to the MovieLens\n20 million data set comprising 20 million ratings of 27K movies by 138K users.\nThe resulting expanded data set has 10 billion ratings, 864K items and 2\nmillion users in its smaller version and can be scaled up or down. A larger\nversion features 655 billion ratings, 7 million items and 17 million users.","url_abs":"http://arxiv.org/abs/1901.08910v3","url_pdf":"http://arxiv.org/pdf/1901.08910v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scalable-realistic-recommendation-datasets","repo_url":"https://github.com/facebookresearch/generative-recommenders","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"scalable-realistic-recommendation-datasets","repo_url":"https://github.com/mlperf/training/tree/master/data_generation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}