{"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-similarity-embedding-for","title":"Collaborative Similarity Embedding for Recommender Systems","arxiv_id":"1902.06188","date":"2019-02-17","proceeding":null,"authors":["Chih-Ming Chen","Chuan-Ju Wang","Ming-Feng Tsai","Yi-Hsuan Yang"],"abstract":"We present collaborative similarity embedding (CSE), a unified framework that\nexploits comprehensive collaborative relations available in a user-item\nbipartite graph for representation learning and recommendation. In the proposed\nframework, we differentiate two types of proximity relations: direct proximity\nand k-th order neighborhood proximity. While learning from the former exploits\ndirect user-item associations observable from the graph, learning from the\nlatter makes use of implicit associations such as user-user similarities and\nitem-item similarities, which can provide valuable information especially when\nthe graph is sparse. Moreover, for improving scalability and flexibility, we\npropose a sampling technique that is specifically designed to capture the two\ntypes of proximity relations. Extensive experiments on eight benchmark datasets\nshow that CSE yields significantly better performance than state-of-the-art\nrecommendation methods.","url_abs":"http://arxiv.org/abs/1902.06188v2","url_pdf":"http://arxiv.org/pdf/1902.06188v2.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-similarity-embedding-for","repo_url":"https://github.com/bdnf/SBX-Recommendation-Engine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"collaborative-similarity-embedding-for","repo_url":"https://github.com/cnclabs/smore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-learning","task_name":"Graph 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/recommendation-systems-on-citeulike","task":"Recommendation Systems","dataset":"CiteULike","model":"RATE-CSE","rank_in_archive_order":1,"of":1,"metrics":{"Recall@10":"0.2362","mAP@10":"0.1452"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-echonest","task":"Recommendation Systems","dataset":"Echonest","model":"RANK-CSE","rank_in_archive_order":1,"of":1,"metrics":{"Recall@10":"0.1358","mAP@10":"0.0679"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-epinions-extend","task":"Recommendation Systems","dataset":"Epinions-Extend","model":"RANK-CSE","rank_in_archive_order":1,"of":1,"metrics":{"Recall@10":"0.1767","mAP@10":"0.0921"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-frappe","task":"Recommendation Systems","dataset":"Frappe","model":"RATE-CSE","rank_in_archive_order":2,"of":2,"metrics":{"Recall@10":"33.47","mAP@10":"0.2047"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-lastfm-360k","task":"Recommendation Systems","dataset":"Last.FM-360k","model":"RANK-CSE","rank_in_archive_order":1,"of":1,"metrics":{"Recall@10":"0.1762","mAP@10":"0.097"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-movielens-latest","task":"Recommendation Systems","dataset":"MovieLens-Latest","model":"RATE-CSE","rank_in_archive_order":1,"of":1,"metrics":{"Recall@10":"0.3225","mAP@10":"0.199"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-netflix","task":"Recommendation Systems","dataset":"Netflix","model":"RATE-CSE","rank_in_archive_order":10,"of":10,"metrics":{"Recall@10":"0.2014","mAP@10":"0.1039"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}