{"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-metric-learning","title":"Collaborative Metric Learning","arxiv_id":null,"date":"2017-04-01","proceeding":"WWW 2017 4","authors":["Cheng-Kang Hsieh","Longqi Yang","Yin Cui","Tsung-Yi Lin","Serge Belongie","Deborah Estrin"],"abstract":"Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users’ preferences but also the user-user and item-item similarity. The proposed algorithm outperforms state-of-the-art collaborative filtering algorithms on a wide range of recommendation tasks and uncovers the underlying spectrum of users’ fine-grained preferences. CML also achieves significant speedup for Top-K recommendation tasks using off-the-shelf, approximate nearest-neighbor search, with negligible accuracy reduction.","url_abs":"https://ylongqi.com/publication/www17b/","url_pdf":"https://ylongqi.com/paper/HsiehYCLBE17.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-metric-learning","repo_url":"https://github.com/changun/CollMetric","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"collaborative-metric-learning","repo_url":"https://github.com/statusrank/LibCML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[{"method_slug":"cpe","method_name":"CPE"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/collaborative-filtering-on-million-song","task":"Recommendation Systems","dataset":"Million Song Dataset","model":"CML","rank_in_archive_order":7,"of":7,"metrics":{"Recall@100":"0.3022","Recall@50":"0.2460"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-1m","task":"Recommendation Systems","dataset":"MovieLens 1M","model":"CML","rank_in_archive_order":21,"of":31,"metrics":{"HR@10":"0.7216","nDCG@10":"0.5413"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-20m","task":"Recommendation Systems","dataset":"MovieLens 20M","model":"CML","rank_in_archive_order":3,"of":18,"metrics":{"HR@10":"0.7764","Recall@100":"0.6022","Recall@50":"0.4665","nDCG@10":"0.5301"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-netflix","task":"Recommendation Systems","dataset":"Netflix","model":"CML","rank_in_archive_order":9,"of":10,"metrics":{"Recall@10":"0.4612","nDCG@10":"0.2948"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}