| COMPUTER SCIENCE AND TECHNOLOGY |
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Hybrid recommendation approach enhanced by deep learning |
| ZHANG Min, DING Biyuan, MA Weizhi, TAN Yunzhi, LIU Yiqun, MA Shaoping |
| State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China |
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Abstract Collaborative filtering based on matrix factorization has been very successful, while cold-start and data sparseness problems have not been well resolved. Hence, many studies have attempted to include review information into rating predictions. This paper presents a hybrid model that introduces deep learning into recommendation system with collaborative filtering. The algorithm combines a stacked denoising auto encoder (SDAE) with a latent factor model (LFM) to make use of both review and rating information to improve the rating predictions. Evaluations on a large, commonly used Amazon dataset show that this approach significantly improves the rating prediction accuracy in comparison with traditional models, with up to 64.43% better predictions.
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| Keywords
deep learning
collaborative filtering
hybrid recommendation
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Issue Date: 15 October 2017
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2017,
Vol. 57
