{"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/collaborative-filtering-with-recurrent-neural","title":"Collaborative Filtering with Recurrent Neural Networks","arxiv_id":"1608.07400","date":"2016-08-26","proceeding":null,"authors":["Robin Devooght","Hugues Bersini"],"abstract":"We show that collaborative filtering can be viewed as a sequence prediction\nproblem, and that given this interpretation, recurrent neural networks offer\nvery competitive approach. In particular we study how the long short-term\nmemory (LSTM) can be applied to collaborative filtering, and how it compares to\nstandard nearest neighbors and matrix factorization methods on movie\nrecommendation. We show that the LSTM is competitive in all aspects, and\nlargely outperforms other methods in terms of item coverage and short term\npredictions.","url_abs":"http://arxiv.org/abs/1608.07400v2","url_pdf":"http://arxiv.org/pdf/1608.07400v2.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":"collaborative-filtering-with-recurrent-neural","repo_url":"https://github.com/rdevooght/sequence-based-recommendations","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"collaborative-filtering-with-recurrent-neural","repo_url":"https://github.com/ROpdam/recommender_comparison","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"movie-recommendation","task_name":"Movie Recommendation"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}