A many objective commercial recommendation algorithm via Game-Based core node extraction

Abstract

The development of the recommendation system (RS) has been focused on improving the accuracy of algorithms. As the number of new users grows, RS suffers from the cold start problem, data sparsity and long-tail problem. In this paper, we proposed a many objective commercial recommendation algorithm that extracts the core node through the pay-off function of game theory and makes it guide the evolutionary computation. Firstly, the core aim of commercial recommendations is to maximize total profit. Profits can vary wildly with the same accuracy. Thus, the profit value as one of the goals is considered in attention to accuracy, coverage, and novelty. Besides, the current user state value is updated according to user features and rating scores matrix with the attention mechanism at different time stamps. The nodes are divided into different communities, forming a dynamic community, and recommendations are made within each community. Meanwhile, to reduce the computational complexity and runtime, the pay-off function of game theory is used to extract the core user in each community. Each contribution value and user interaction as criteria to guide the current community of users. Experiments on real datasets show that selecting core nodes can improve the performance of business RS. Compared with other existing algorithms in many experiments, our algorithm performs better.