CDMF: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation

Date
2019-01-08
Authors
Gan, Mingxin
Ma, Yingxue
Xiao, Kejun
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Abstract
We 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.
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Data, Text, and Web Mining for Business Analytics, Decision Analytics, Mobile Services, and Service Science, Context-aware Recommendation, Convolutional Neural Network, Deep Learning, Information Fusion, Tags
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