A hypergraph-based framework for personalized recommendations via user preference and dynamics clustering
作者：
Highlights：
• Hypergraph reduces complexity and preserves high-order structure of users and items.
• Attention relations of users to items considers personal preferences.
• Novel similarity filters out the items that the two users have different opinions.
• Dynamics clustering approach combines with game-theoretic payoff function.
摘要
•Hypergraph reduces complexity and preserves high-order structure of users and items.•Attention relations of users to items considers personal preferences.•Novel similarity filters out the items that the two users have different opinions.•Dynamics clustering approach combines with game-theoretic payoff function.
论文关键词：Hypergraph model,Attention relations,Payoff function,Similarity measurement,Recommendation system
论文评审过程：Received 19 December 2021, Revised 4 May 2022, Accepted 7 May 2022, Available online 19 May 2022, Version of Record 24 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117552
