Gan, MingxinMa, YingxueXiao, Kejun2019-01-022019-01-022019-01-08978-0-9981331-2-6http://hdl.handle.net/10125/59552We proposes a novel deep neural network based recommendation model named Convolutional and Dense-layer Matrix Factorization (CDMF) for Context-aware recommendation, which is to combine multi-source information from item description and tag information. CDMF adopts a convolution neural network to extract hidden feature from item description as document and then fuses it with tag information via a full connection layer, thus generates a comprehensive feature vector. Based on the matrix factorization method, CDMF makes rating prediction based on the fused information of both users and items. Experiments on a real dataset show that the proposed deep learning model obviously outperforms the state-of-art recommendation methods.8 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalData, Text, and Web Mining for Business AnalyticsDecision Analytics, Mobile Services, and Service ScienceContext-aware Recommendation, Convolutional Neural Network, Deep Learning, Information Fusion, TagsConference Paper10.24251/HICSS.2019.138