Delayed evolutionary game clustering-based recommendation algorithm via latent information and user preference

Abstract

Recommendation system is widely used because of its personalized service. It helps users obtain satisfactory results due to ambiguous expression in search engines. However, with the increasing number of users and items, most of the existing recommendation algorithms have problems of cold start, sparse data and high complexity. To address the above issues, this paper applies the rating information and attribute information of users and items to better represent the preferences of users and the activity of items by enriching the available information. Furthermore, we propose a novel game-based evolutionary clustering method to divide interest communities for users, which not only reduces the complexity of recommendation, but also takes full account of users’ preferences. In addition, the impact of delayed information transmission on experimental performance is considered in the game-based evolutionary clustering. Since the calculation of similarity plays a significant role in finding the nearest neighbors of target users in the community, we propose a new similarity measurement strategy based on user preferences. Finally, the effectiveness of the proposed algorithm is verified by ablation experiment and comparison experiments. The experimental results illustrate that our algorithm outperforms the existing excellent algorithms.