A Collaborative Filtering Algorithm Based on Sentiment Analysis in Review Texts

To improve the performance of collaborative filtering algorithm, a collaborative filtering algorithm based on sentiment analysis in review texts (CF_SA) is proposed in this paper. First, LDA (Latent Dirichlet Allocation) is used to form the user topic feature matrix and calculate user review similarity. Secondly, the ALBERT(A Lite Bidirectional Encoder Representation from Transformers) model and BiLSTM(Bi-directional Long Short-Term Memory) neural network are used to mine users' emotional tendencies in item review texts, improve the user rating table, and calculate user rating similarity. Next, the user review similarity and user rating similarity are combined to obtain the final user similarity and predict the user's rating for the item. Finally, experiments were conducted on the Douban Film Review dataset. Compared with classic recommendation algorithms, the results show that the proposed algorithm has good recommendation performance.