Fair multi-stakeholder news recommender system with hypergraph ranking
作者：
Highlights：
• Hypergraph learning has the natural capability of handling a multi-stakeholder recommendation task.
• A hypergraph-based recommender can be adapted to address fairness in a temporal-aware setting.
• A hypergraph based recommender can be adapted to address the filter bubble phenomenon by diversifying the recommendation lists.
摘要
•Hypergraph learning has the natural capability of handling a multi-stakeholder recommendation task.•A hypergraph-based recommender can be adapted to address fairness in a temporal-aware setting.•A hypergraph based recommender can be adapted to address the filter bubble phenomenon by diversifying the recommendation lists.
论文关键词：Multi-stakeholder recommender systems,Hypergraph learning,News recommendation,Fair recommender systems,Diversity-aware recommender systems
论文评审过程：Received 30 November 2020, Revised 30 March 2021, Accepted 15 June 2021, Available online 29 June 2021, Version of Record 29 June 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102663
