Deep Collaborative Filtering Recommendation Algorithm Based on Sentiment Analysis
Dun Ao, Cong Zhang · 2023
Data sparsity is the main challenge that recommendation algorithms have been facing. E-commerce recommendation mainly focus on the explicit interaction between users and items to alleviate this problem, while ignoring the implicit feature of the hidden sentiment information in review texts. To address the above issues, a deep collaborative filtering recommendation model based on sentiment analysis of reviews (SA-NCF) is proposed. First, the sentiment information in user reviews is extracted for quantitative processing. Then the implicit feature matrix of users and items is constructed by combining with the explicit feature of user ratings. Finally, the whole model is nonlinear processed by Matrix Factorization and Multi-layer Perceptron to improve the performance of the recommender system. Experiments are conducted on the JD public dataset and compared with several classical models. The results show that the effectiveness and accuracy of the recommendation algorithm with sentiment factors are better than the comparison model.