Recommendation System Based on Deep Sentiment Analysis and Matrix Factorization

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In order to solve the problem of data sparsity and credibility in collaborative filtering, a recommendation system based on sentiment analysis and matrix factorization (SAMF) is proposed in this paper, which uses topic model and deep learning technology to fully mine the implicit information in reviews to improve the rating matrix and assist recommendation. Firstly, user topic distribution and item topic distribution are generated from reviews(consisting user reviews and item reviews) through LDA(Latent Dirichlet Allocation). The user feature matrix and item feature matrix are created based on topic probability. Secondly, user feature matrix and item feature matrix are integrated to create user-item preference matrix. Thirdly, the user-item preference matrix and the original rating matrix are integrated to create the user-item rating matrix. Fourthly, BERT(Bidirectional Encoder Representation from Transformers) is used to quantify the sentiment information contained in the reviews and integrate the sentiment information with the user-item rating matrix, to modify and update the user-item rating matrix. Finally, the updated user-item rating matrix is used to achieve rating prediction and Top-N recommendation. Experiments on Amazon datasets demonstrates that the proposed SAMF has better recommendation performance than other classical algorithms.