{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T21:54:20Z","timestamp":1740174860737,"version":"3.37.3"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T00:00:00Z","timestamp":1613520000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066010","61966009","U1811264","U1711263","2020GXNSFAA159055","AA17202024","2020YCXS047"],"award-info":[{"award-number":["62066010","61966009","U1811264","U1711263","2020GXNSFAA159055","AA17202024","2020YCXS047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Natural Science Foundation of Guangxi Province","doi-asserted-by":"publisher","award":["62066010","61966009","U1811264","U1711263","2020GXNSFAA159055","AA17202024","2020YCXS047"],"award-info":[{"award-number":["62066010","61966009","U1811264","U1711263","2020GXNSFAA159055","AA17202024","2020YCXS047"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Innovation-Driven Development Grand Project","award":["62066010","61966009","U1811264","U1711263","2020GXNSFAA159055","AA17202024","2020YCXS047"],"award-info":[{"award-number":["62066010","61966009","U1811264","U1711263","2020GXNSFAA159055","AA17202024","2020YCXS047"]}]},{"name":"Innovation Project of GUET Graduate Education","award":["62066010","61966009","U1811264","U1711263","2020GXNSFAA159055","AA17202024","2020YCXS047"],"award-info":[{"award-number":["62066010","61966009","U1811264","U1711263","2020GXNSFAA159055","AA17202024","2020YCXS047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,2,17]]},"abstract":"<jats:p>Deep learning systems have been phenomenally successful in the fields of computer vision, speech recognition, and natural language processing. Recently, researchers have adopted deep learning techniques to tackle collaborative filtering with implicit feedback. However, the existing methods generally profile both users and items directly, while neglecting the similarities between users\u2019 and items\u2019 neighborhoods. To this end, we propose the neighborhood attentional memory networks (NAMN), a deep learning recommendation model applying two dedicated memory networks to capture users\u2019 neighborhood relations and items\u2019 neighborhood relations respectively. Specifically, we first design the user neighborhood component and the item neighborhood component based on memory networks and attention mechanisms. Then, by the associative addressing scheme with the user and item memories in the neighborhood components, we capture the complex user-item neighborhood relations. Stacking multiple memory modules together yields deeper architectures exploring higher-order complex user-item neighborhood relations. Finally, the output module jointly exploits the user and item neighborhood information with the user and item memories to obtain the ranking score. Extensive experiments on three real-world datasets demonstrate significant improvements of the proposed NAMN method over the state-of-the-art methods.<\/jats:p>","DOI":"10.1155\/2021\/8880331","type":"journal-article","created":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T02:58:07Z","timestamp":1613617087000},"page":"1-10","source":"Crossref","is-referenced-by-count":1,"title":["Neighborhood Attentional Memory Networks for Recommendation Systems"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1662-7110","authenticated-orcid":true,"given":"Tianlong","family":"Gu","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China"},{"name":"College of Information Science and Technology, College of Cyber Security, Jinan University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5786-9493","authenticated-orcid":true,"given":"Hongliang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China"},{"name":"Guangxi Key Lab of Trusted Software, Guilin University of Electronic Technology, Guilin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7200-0929","authenticated-orcid":true,"given":"Chenzhong","family":"Bin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China"},{"name":"Guangxi Key Lab of Trusted Software, Guilin University of Electronic Technology, Guilin, China"}]},{"given":"Liang","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China"},{"name":"Guangxi Key Lab of Trusted Software, Guilin University of Electronic Technology, Guilin, China"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China"},{"name":"Guangxi Key Lab of Trusted Software, Guilin University of Electronic Technology, Guilin, China"}]}],"member":"311","reference":[{"author":"H. Zhang","doi-asserted-by":"crossref","article-title":"Discrete collaborative filtering","key":"1","DOI":"10.1145\/2911451.2911502"},{"doi-asserted-by":"publisher","key":"2","DOI":"10.1109\/mic.2003.1167344"},{"author":"Y. Koren","doi-asserted-by":"crossref","article-title":"Factorization meets the neighborhood: a multifaceted collaborative filtering model","key":"3","DOI":"10.1145\/1401890.1401944"},{"author":"S. Rendle","doi-asserted-by":"crossref","article-title":"Factorization machines","key":"4","DOI":"10.1109\/ICDM.2010.127"},{"doi-asserted-by":"publisher","key":"5","DOI":"10.1609\/aaai.v33i01.33015941"},{"author":"B. Liu","first-page":"1119","article-title":"Feature generation by convolutional neural network for click-through rate prediction","key":"6"},{"author":"H. Wang","doi-asserted-by":"crossref","article-title":"DKN: deep knowledge-aware network for news recommendation","key":"7","DOI":"10.1145\/3178876.3186175"},{"author":"H. 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Xin","doi-asserted-by":"crossref","article-title":"CFM: convolutional factorization machines for context-aware recommendation","key":"15","DOI":"10.24963\/ijcai.2019\/545"},{"author":"X. Zhou","article-title":"Collaborative metric learning with memory network for multi-relational recommender systems","key":"16"},{"author":"C. Chen","doi-asserted-by":"crossref","article-title":"Social attentional memory network: modeling aspect- and friend-level differences in recommendation","key":"17","DOI":"10.1145\/3289600.3290982"},{"author":"Z. Yu","article-title":"Adaptive user modeling with long and short-term preferences for personalized recommendation","key":"18"},{"author":"Y. Gong","article-title":"Hashtag recommendation using attention-based convolutional neural network","key":"19"},{"year":"2014","author":"W. Zaremba","article-title":"Learning to execute","key":"20"},{"author":"J. Weston","article-title":"Memory networks","key":"21"},{"author":"S. Sukhbaatar","first-page":"2440","article-title":"End-to-end memory networks","key":"22"},{"author":"H. Huang","first-page":"1872","article-title":"Mention recommendation for twitter with end-to-end memory network","key":"23"},{"author":"X. Chen","first-page":"108","article-title":"Sequential recommendation with user memory networks","key":"24"},{"author":"T. Ebesu","first-page":"515","article-title":"Collaborative memory network for recommendation systems","key":"25"},{"unstructured":"EbesuT. A.Deep learning for recommender systems201922Santa Clara, CA, USASanta Clara UniversityEngineering Ph.D. theses","key":"26"},{"key":"27","first-page":"957","article-title":"AutoSVD++: an effeicient hybrid collaborative filtering model via contractive auto-encoders","volume":"51","author":"S. Zhang","year":"2017","journal-title":"ACM SIGIR Forum"},{"author":"S. 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Ricci","year":"2011"}],"container-title":["Scientific Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/8880331.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/8880331.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/8880331.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T02:58:18Z","timestamp":1613617098000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/sp\/2021\/8880331\/"}},"subtitle":[],"editor":[{"given":"Qianchuan","family":"Zhao","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,2,17]]},"references-count":30,"alternative-id":["8880331","8880331"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8880331","relation":{},"ISSN":["1875-919X","1058-9244"],"issn-type":[{"type":"electronic","value":"1875-919X"},{"type":"print","value":"1058-9244"}],"subject":[],"published":{"date-parts":[[2021,2,17]]}}}