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However, a large amount of research focuses on the consistency between the rating-based latent factor and review-based latent factor. But in fact, these two parts are completely different. In this article, the authors propose a model named collaboration matrix factorization (CMF) that combines a projection method with a convolutional matrix factorization (ConvMF) to extract the collaboration between rating-based latent factors and review-based latent factors that comes from the results of the CNN process. Extensive experiments on three real-world datasets show that the projection method achieves significant improvements over the existing baseline.<\/p>","DOI":"10.4018\/jdm.2019040102","type":"journal-article","created":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T10:08:56Z","timestamp":1561716536000},"page":"27-43","source":"Crossref","is-referenced-by-count":7,"title":["Collaboration Matrix Factorization on Rate and Review for Recommendation"],"prefix":"10.4018","volume":"30","author":[{"given":"Zhicheng","family":"Wu","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huafeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7174-6588","authenticated-orcid":true,"given":"Yanyan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Jing","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"JDM.2019040102-0","unstructured":"Adams, R. 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