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Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional recommender systems that use a single rating. In this work, we introduce a multi-criteria collaborative filtering recommender by combining deep neural network and matrix factorization. Our model consists of two parts: the first part uses a fused model of deep neural network and matrix factorization to predict the criteria ratings and the second one employs a deep neural network to predict the overall rating. The experimental results on two datasets, including a real-world dataset, show that the proposed model outperformed several state-of-the-art methods across different datasets and performance evaluation metrics.<\/jats:p>","DOI":"10.1186\/s40537-020-00309-6","type":"journal-article","created":{"date-parts":[[2020,5,24]],"date-time":"2020-05-24T13:02:24Z","timestamp":1590325344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization"],"prefix":"10.1186","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0449-6835","authenticated-orcid":false,"given":"Nour","family":"Nassar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Assef","family":"Jafar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasser","family":"Rahhal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,5,24]]},"reference":[{"key":"309_CR1","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.knosys.2018.12.022","volume":"166","author":"J Zhao","year":"2019","unstructured":"Zhao J, Geng X, Zhou J, Sun Q, Xiao Y, Zhang Z, Fu Z. 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