{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:45:51Z","timestamp":1753875951116,"version":"3.41.2"},"reference-count":32,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Collaborative filtering (CF) is a well-known and eminent recommendation technique to predict the preference of new users by revealing the structures of historical records of the examined users. Even though CF is effectively adapted in several commercial areas, many limitations still exist, particularly in the sparsity of rating data that raises many issues. This paper devises a novel deep learning strategy for CF to recognize user preferences. Here, black hole entropic fuzzy clustering (BHEFC) is devised for clustering item sequences to form groups with similar item sequences. Moreover, cluster centroids are optimized using the tunicate swarm magnetic optimization algorithm (TSMOA), which is devised by combining tunicate swarm algorithm and magnetic optimization algorithm. After grouping similar items together, the group matching is performed based on a deep convolutional neural network (Deep CNN). Subsequently, the visitor sequence and query sequence are compared using Jaro\u2013Winkler distance, which contributes to the best visitor sequence. From this best visitor sequence, the recommended product is acquired. The proposed TSMOA\u2013BHEFC and Deep CNN outperformed other methods with minimal mean absolute error of 0.200, mean absolute percentage error of 0.198 and root mean square error of 0.447, respectively.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab098","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T19:12:22Z","timestamp":1624561942000},"source":"Crossref","is-referenced-by-count":1,"title":["Tunicate Swarm Magnetic Optimization With Deep Convolution Neural Network For Collaborative Filter Recommendation"],"prefix":"10.1093","author":[{"given":"Shefali","family":"Gupta","sequence":"first","affiliation":[{"name":"Research Scholar, Jagannath University, Jaipur, Rajasthan 302022, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankit","family":"Goel","sequence":"additional","affiliation":[{"name":"Knowledge Expert, Boston Consulting Group, Gurugram, Haryana 122002, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dr Meenu","family":"Dave","sequence":"additional","affiliation":[{"name":"Research Scholar, Jagannath University, Jaipur, Rajasthan 302022, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"2021081613492344200_ref1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/2.666847","article-title":"Ganging up on information overload","volume":"31","author":"Borchers","year":"1998","journal-title":"Computer"},{"key":"2021081613492344200_ref2","doi-asserted-by":"crossref","first-page":"11501","DOI":"10.1007\/s10586-017-1414-2","article-title":"Gauss-core extension dependent prediction algorithm for collaborative filtering recommendation","volume":"22","author":"Xu","year":"2019","journal-title":"Clust. 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Intel."},{"key":"2021081613492344200_ref32","article-title":"Retailrocket recommender system dataset"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/comjnl\/advance-article-pdf\/doi\/10.1093\/comjnl\/bxab098\/39761618\/bxab098.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/comjnl\/advance-article-pdf\/doi\/10.1093\/comjnl\/bxab098\/39761618\/bxab098.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T13:52:04Z","timestamp":1629121924000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/advance-article\/doi\/10.1093\/comjnl\/bxab098\/6323602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,19]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxab098","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"type":"print","value":"0010-4620"},{"type":"electronic","value":"1460-2067"}],"subject":[],"published":{"date-parts":[[2021,7,19]]},"article-number":"bxab098"}}