{"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/hyperbolic-recommender-systems","title":"HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems","arxiv_id":"1809.01703","date":"2018-09-05","proceeding":null,"authors":["Lucas Vinh Tran","Yi Tay","Shuai Zhang","Gao Cong","Xiao-Li Li"],"abstract":"This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the formalism of the spaces could be utilized to generalize the most common Euclidean vector operations. Overall, this work aims to bridge the gap between Euclidean and hyperbolic geometry in recommender systems through metric learning approach. We propose HyperML (Hyperbolic Metric Learning), a conceptually simple but highly effective model for boosting the performance. Via a series of extensive experiments, we show that our proposed HyperML not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in hyperbolic geometry.","url_abs":"https://arxiv.org/abs/1809.01703v3","url_pdf":"https://arxiv.org/pdf/1809.01703v3.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":[],"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"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/collaborative-filtering-on-movielens-1m","task":"Recommendation Systems","dataset":"MovieLens 1M","model":"HyperML","rank_in_archive_order":19,"of":31,"metrics":{"HR@10":"0.7563","nDCG@10":"0.5620"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-20m","task":"Recommendation Systems","dataset":"MovieLens 20M","model":"HyperML","rank_in_archive_order":1,"of":18,"metrics":{"HR@10":"0.8736","nDCG@10":"0.6404"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.01703","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}