Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings

作者：Wang, Heyong^*; Hong, Ming; Hong, Zhenqin

来源：IEEE Access, 2021, 9: 86728-86738.

DOI：10.1109/ACCESS.2021.3080079

• Neural networks
• Feature extraction
• Data mining
• Data models
• Fuses
• Prediction algorithms
• Data structures
• BP neural network
• data fusion
• data sparsity
• recommendation system
• topic mining

Sparsity of rating data is a severe problem to be solved in modern recommendation researches. The fusion recommendation method is an effective solution for the problem. The method combines rating data and other types of user feedback data, such as reviews and image, to improve performance of the traditional recommendation algorithms. Some researchers have proposed fusion recommendation algorithms based on BP (Back Propagation) neural network and achieved better results. However, some existing fusion recommendation algorithms based on BP neural network still have some shortcomings. They rely on the assistance of the traditional recommendation algorithms. Moreover, the high complexity of the fusion processes of these algorithms possibly has negative impacts on the fusion effects. In this paper, we modify the fusion recommendation algorithm and propose the NNFR (neural networks fusion recommendation) model. This model improves the structure of BP neural network by specially designing the structure of network layers. User reviews and ratings can be processed in two separate sub-networks respectively and further fused in the fusion layer. The fusion features of user reviews and ratings are directly applied to perform recommendation, in order to avoid the assistance of the traditional recommendation algorithms and improve the fusing efficiency and quality. Experimental results indicate that the outstanding performance of NNFR model than comparative recommendation algorithms on rating predictions and top-k recommendations. Moreover, NNFR model can still produce high-quality recommendation results in the scenarios of sparse data.