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The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.<\/jats:p>","DOI":"10.1155\/2022\/9593957","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T21:20:10Z","timestamp":1641849610000},"page":"1-12","source":"Crossref","is-referenced-by-count":15,"title":["Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3015-054X","authenticated-orcid":true,"given":"Huazhen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information & Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2258-8330","authenticated-orcid":true,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information & Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"},{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information & Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renqian","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Information & Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqi","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Information & Electrical Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Hebei University of Engineering, Handan 056038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1145\/1871437.1871643"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2005.99"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/icdm.2008.22"},{"key":"4","first-page":"1257","article-title":"Probabilistic matrix factorization","author":"R. 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