{"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/outer-product-based-neural-collaborative","title":"Outer Product-based Neural Collaborative Filtering","arxiv_id":"1808.03912","date":"2018-08-12","proceeding":null,"authors":["Xiangnan He","Xiaoyu Du","Xiang Wang","Feng Tian","Jinhui Tang","Tat-Seng Chua"],"abstract":"In this work, we contribute a new multi-layer neural network architecture\nnamed ONCF to perform collaborative filtering. The idea is to use an outer\nproduct to explicitly model the pairwise correlations between the dimensions of\nthe embedding space. In contrast to existing neural recommender models that\ncombine user embedding and item embedding via a simple concatenation or\nelement-wise product, our proposal of using outer product above the embedding\nlayer results in a two-dimensional interaction map that is more expressive and\nsemantically plausible. Above the interaction map obtained by outer product, we\npropose to employ a convolutional neural network to learn high-order\ncorrelations among embedding dimensions. Extensive experiments on two public\nimplicit feedback data demonstrate the effectiveness of our proposed ONCF\nframework, in particular, the positive effect of using outer product to model\nthe correlations between embedding dimensions in the low level of multi-layer\nneural recommender model. The experiment codes are available at:\nhttps://github.com/duxy-me/ConvNCF","url_abs":"http://arxiv.org/abs/1808.03912v1","url_pdf":"http://arxiv.org/pdf/1808.03912v1.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":"outer-product-based-neural-collaborative","repo_url":"https://github.com/duxy-me/ConvNCF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"multi-domain-recommender-systems","task_name":"Multi-Domain Recommender Systems"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/recommendation-systems-on-gowalla","task":"Recommendation Systems","dataset":"Gowalla","model":"ConvNCF","rank_in_archive_order":1,"of":13,"metrics":{"nDCG@20":"0.6019"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-yelp","task":"Recommendation Systems","dataset":"Yelp","model":"ConvNCF","rank_in_archive_order":2,"of":2,"metrics":{"NDCG@20":"0.1939"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.03912","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}