{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:19:03Z","timestamp":1753881543782,"version":"3.41.2"},"reference-count":11,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":201,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Projects of Natural Science Research in Universities of Anhui Province","award":["KJ2020A0681","KJ2019A0682"],"award-info":[{"award-number":["KJ2020A0681","KJ2019A0682"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Probability matrix factorization model can be used to solve the problem of high\u2010dimensional sparsity of user and rating data in the recommender systems. However, most of the existing methods use the user to model the item rating, ignoring the relationship between the user and the item, so the accuracy of user\u2010item rating prediction is still low. Therefore, this paper proposes a probabilistic matrix factorization model based on BP neural network ensemble learning, bagging, and fuzzy clustering. Firstly, the membership function of fuzzy clustering and the selection of cluster center are used to calculate the user\u2010item rating matrix; secondly, BP neural network trains the user\u2010item scoring matrix after clustering, further improving the accuracy of scoring prediction; finally, the bagging method in ensemble learning is introduced, which takes the number of user\u2010item scores as the base learner, trains the base learner through BP neural network, and finally obtains the score prediction through the voting results, which improves the stability of the model. Compared with the existing PMF models, the root mean square error of the PMF model after fuzzy clustering is increased by 9.27% and 3.95%, and the average absolute error is increased by 21.14% and 1.11%, respectively; then, the performance of the first mock exam is introduced. The root mean square error of the ensemble method is increased by 4.02% and 0.42%, respectively, compared with the existing single model. Finally, the weights of BP neural network training based learner are introduced to improve the accuracy of the model, which also verifies the universality of the model.<\/jats:p>","DOI":"10.1155\/2021\/9985894","type":"journal-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T22:50:08Z","timestamp":1626907808000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4391-5919","authenticated-orcid":false,"given":"Zhengjin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siwei","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baojin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiabao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7106-6993","authenticated-orcid":false,"given":"Baohua","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/mc.2009.263"},{"key":"e_1_2_10_2_2","unstructured":"SalakhutdinovR.andMnihA. Probabilistic matrix factorization Proceedings of the International Conference on Neural Information Processing Systems December 2007 Vancouver Canada."},{"key":"e_1_2_10_3_2","doi-asserted-by":"crossref","unstructured":"SalakhutdinovR.andMnihA. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo Proceedings of the International Conference on Machine Learning July 2008 Helsinki Finland https:\/\/doi.org\/10.1145\/1390156.1390267.","DOI":"10.1145\/1390156.1390267"},{"key":"e_1_2_10_4_2","doi-asserted-by":"crossref","unstructured":"AkulwarP.andPardeshiS. Bayesian probabilistic matrix factorization\u2014a dive towards recommendation Proceedings of the International Conference on Inventive Computation Technologies IEEE August 2016 Coimbatore India.","DOI":"10.1109\/INVENTIVE.2016.7830213"},{"key":"e_1_2_10_5_2","first-page":"1","article-title":"Personalized recommendation algorithm based on Ensemble Learning","volume":"47","author":"Fang Y.","year":"2011","journal-title":"Computer Engineering and Application"},{"key":"e_1_2_10_6_2","first-page":"62","article-title":"Extreme gradient boosting recommendation algorithm with collaborative filtering","volume":"37","author":"Cui Y.","year":"2020","journal-title":"Computer Application Research"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2817-3"},{"volume-title":"Ensemble Learning","year":"2009","author":"Hastie T.","key":"e_1_2_10_8_2"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.18178\/ijmlc.2019.9.2.775"},{"key":"e_1_2_10_10_2","doi-asserted-by":"crossref","unstructured":"IchihashiH. 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FCM classifier for high-dimensional data Proceedings of the IEEE International Conference on Fuzzy Systems June 2008 New Orleans LA USA https:\/\/doi.org\/10.1109\/fuzzy.2008.4630366 2-s2.0-55249094591.","DOI":"10.1109\/FUZZY.2008.4630366"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.19026\/rjaset.6.4092"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/9985894.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/9985894.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/9985894","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:56:15Z","timestamp":1723244175000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/9985894"}},"subtitle":[],"editor":[{"given":"Muhammad","family":"Javaid","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":11,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/9985894"],"URL":"https:\/\/doi.org\/10.1155\/2021\/9985894","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"type":"print","value":"1076-2787"},{"type":"electronic","value":"1099-0526"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-05-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9985894"}}