{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:09:40Z","timestamp":1783184980747,"version":"3.54.6"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users\/items as in- dependent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users\/items are related with various couplings existing within and between users and items, which may better ex- plain how and why a user has personalized pref- erence on an item. This work builds on non- IID learning to propose a neural user-item cou- pling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within\/between users and items w.r.t. user\/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recom- menders: neural matrix factorization and Google\u2019s Wide&amp;Deep network.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/509","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"3662-3668","source":"Crossref","is-referenced-by-count":47,"title":["CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering"],"prefix":"10.24963","author":[{"given":"Quangui","family":"Zhang","sequence":"first","affiliation":[{"name":"Liaoning Technical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longbing","family":"Cao","sequence":"additional","affiliation":[{"name":"University of Technology Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengzhang","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Liaoning Technical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinguang","family":"Sun","sequence":"additional","affiliation":[{"name":"Liaoning Technical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:53:32Z","timestamp":1530770012000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/509"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/509","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}