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.

本文构建了一种基于用户评论的增强潜在语义模型，并利用正则化因子捕捉用户对每种商品的潜在话题的时间演化特征，从而提高推荐的准确性。还改进了多个偏好特征的自适应时间加权，利用人类遗忘特征、项目兴趣重叠和评论文本语义级别的相似性计算不同用户在不同时间段的偏好，以提高稀疏评价数据的准确性。论文在九个数据集上与六种基于时间矩阵的分解基线方法进行对比实验，结果表明准确率比TimeSVD++好31.64%，比BTMF好21.08%，比TMRevCo好15.51%，比BPTF好13.99%， 9.