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