On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms

作者：

Highlights：

• We propose a novel hybrid Collaborative Filtering algorithm to counter bribing.

• We identify, from the point of view of a seller of an item, which users are profitable to bribe.

• We show that our algorithm is as effective as the state-of-the-art approaches, while being more efficient.

• We illustrate our framework, by studying the impact of bribing in our algorithm and a real-world system.

摘要

•We propose a novel hybrid Collaborative Filtering algorithm to counter bribing.•We identify, from the point of view of a seller of an item, which users are profitable to bribe.•We show that our algorithm is as effective as the state-of-the-art approaches, while being more efficient.•We illustrate our framework, by studying the impact of bribing in our algorithm and a real-world system.

论文关键词：Bribing,Algorithmic bias,Social influence

论文评审过程：Received 15 October 2018, Revised 12 June 2019, Accepted 13 June 2019, Available online 3 July 2019, Version of Record 13 January 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2019.102058