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dc.contributor.authorQuang Hung Leen_US
dc.contributor.authorToan Nguyen Mauen_US
dc.contributor.authorRoengchai Tansuchaten_US
dc.contributor.authorVan Nam Huynhen_US
dc.description.abstractThis paper addresses the problem of multi-criteria recommendation in the hotel industry. The main focus is to analyze user preferences from different aspects based on multi-criteria ratings and develop a new multi-criteria collaborative filtering method for hotel recommendations. Particularly, the proposed recommendation system integrates matrix factorization into a deep learning model to predict the multi-criteria ratings, and then the evidential reasoning approach is adopted to model the uncertainty of those ratings represented as mass functions in Dempster-Shafer theory of evidence. Finally, Dempster's rule of combination is utilized to aggregate those multi-criteria ratings to obtain the overall rating for recommendation. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness and efficiency of the proposed method compared with other multi-criteria collaborative filtering methods.en_US
dc.subjectComputer Scienceen_US
dc.subjectMaterials Scienceen_US
dc.titleA Multi-Criteria Collaborative Filtering Approach Using Deep Learning and Dempster-Shafer Theory for Hotel Recommendationsen_US
article.title.sourcetitleIEEE Accessen_US
article.volume10en_US Advanced Institute of Science and Technologyen_US Mai Universityen_US Nhon Universityen_US
Appears in Collections:CMUL: Journal Articles

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