No abstract

Sparsity of rating data is a severe problem to be solved in modern recommendation researches. The fusion recommendation method is an effective solution to 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 some 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. And the high complexity of the fusion process of these algorithms would have a negative impact on the fusion effect. 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 to avoid the assistance of the traditional recommendation algorithms and improve the fusing efficiency and quality. Experimental results show that the NNFR model can achieve better predictions than comparative recommendation algorithms to generate more accurate topk recommendation lists for users. Moreover, NNFR model can still produce high-quality recommendation results in the scenarios of sparse data.