TDTMF: A recommendation model based on user temporal interest drift and latent review topic evolution with regularization factor |
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| Institution: | 1. West China Biomedical Big Data Center, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;2. Department of Radiology, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;3. West China Periodicals, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;4. Department of Bile Duct Surgery, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;1. Institute of Environmental Science and Technology, Universitat Autònoma de Barcelona, Spain;2. Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russian Federation |
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| Abstract: | This paper constructs a novel enhanced latent semantic model based on users’ comments, and employs regularization factors to capture the temporal evolution characteristics of users’ potential topics for each commodity, so as to improve the accuracy of recommendation. The adaptive temporal weighting of multiple preference features is also improved to calculate the preferences of different users at different time periods using human forgetting features, item interest overlap, and similarity at the semantic level of the review text to improve the accuracy of sparse evaluation data. The paper conducts comparison experiments with six temporal matrix-based decomposition baseline methods in nine datasets, and the results show that the accuracy is 31.64% better than TimeSVD++, 21.08% better than BTMF, 15.51% better than TMRevCo, 13.99% better than BPTF, 9.24% better than TCMF, and 3.19% better than MUTPD ,which indicates that the model is more effective in capturing users’ temporal interest drift and better reflects the evolutionary relationship between users’ latent topics and item ratings. |
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| Keywords: | Time series Interest drift Latent factor Topic evolution Recommender systems Matrix factorization |
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