TDTMF: A recommendation model based on user temporal interest drift and latent review topic evolution with regularization factor

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.