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
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.
张敏, 丁弼原, 马为之, 谭云志, 刘奕群, 马少平. 基于深度学习加强的混合推荐方法[J]. 清华大学学报(自然科学版), 2017, 57(10): 1014-1021.
ZHANG Min, DING Biyuan, MA Weizhi, TAN Yunzhi, LIU Yiqun, MA Shaoping. Hybrid recommendation approach enhanced by deep learning. Journal of Tsinghua University(Science and Technology), 2017, 57(10): 1014-1021.
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