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Inf. Syst."],"published-print":{"date-parts":[[2019,10,31]]},"abstract":"<jats:p>We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization that takes both implicit and explicit feedback, point-wise and pair-wise loss into account.<\/jats:p>\n          <jats:p>Experiments on several real-world datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24%, and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.<\/jats:p>","DOI":"10.1145\/3343117","type":"journal-article","created":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T19:05:16Z","timestamp":1565809516000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":124,"title":["Joint Neural Collaborative Filtering for Recommender Systems"],"prefix":"10.1145","volume":"37","author":[{"given":"Wanyu","family":"Chen","sequence":"first","affiliation":[{"name":"National University of Defense Technology, China and University of Amsterdam, Amsterdam, The Netherlands"}]},{"given":"Fei","family":"Cai","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"given":"Honghui","family":"Chen","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"given":"Maarten De","family":"Rijke","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2019,8,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aci.2014.10.001"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_3_1","first-page":"17","article-title":"Deep learning methods on recommender system: A survey of state-of-the-art.Int","volume":"162","author":"Betru Basiliyos Tilahun","year":"2017","unstructured":"Basiliyos Tilahun Betru , Charles Awono Onana , and Batchakui Bernabe . 2017 . 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