{"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-recommendation-with-poisson","title":"Scalable Recommendation with Poisson Factorization","arxiv_id":"1311.1704","date":"2013-11-07","proceeding":null,"authors":["Prem Gopalan","Jake M. Hofman","David M. Blei"],"abstract":"We develop a Bayesian Poisson matrix factorization model for forming\nrecommendations from sparse user behavior data. These data are large user/item\nmatrices where each user has provided feedback on only a small subset of items,\neither explicitly (e.g., through star ratings) or implicitly (e.g., through\nviews or purchases). In contrast to traditional matrix factorization\napproaches, Poisson factorization implicitly models each user's limited\nattention to consume items. Moreover, because of the mathematical form of the\nPoisson likelihood, the model needs only to explicitly consider the observed\nentries in the matrix, leading to both scalable computation and good predictive\nperformance. We develop a variational inference algorithm for approximate\nposterior inference that scales up to massive data sets. This is an efficient\nalgorithm that iterates over the observed entries and adjusts an approximate\nposterior over the user/item representations. We apply our method to large\nreal-world user data containing users rating movies, users listening to songs,\nand users reading scientific papers. In all these settings, Bayesian Poisson\nfactorization outperforms state-of-the-art matrix factorization methods.","url_abs":"http://arxiv.org/abs/1311.1704v3","url_pdf":"http://arxiv.org/pdf/1311.1704v3.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-recommendation-with-poisson","repo_url":"https://github.com/premgopalan/hgaprec","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"scalable-recommendation-with-poisson","repo_url":"https://github.com/ekanshs/graphex-nnmf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"scalable-recommendation-with-poisson","repo_url":"https://github.com/PreferredAI/cornac","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"scalable-recommendation-with-poisson","repo_url":"https://github.com/david-cortes/hpfrec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1311.1704","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}