MulSetRank: Multiple set ranking for personalized recommendation from implicit feedback

作者：

Highlights：

• We propose a novel setwise recommendation framework.

• We develop a new scheme to mine users’ potential preference items.

• We propose a sampling scheme to approximate the top one probability.

• We conduct extensive experiments to validate the effectiveness.

摘要

•We propose a novel setwise recommendation framework.•We develop a new scheme to mine users’ potential preference items.•We propose a sampling scheme to approximate the top one probability.•We conduct extensive experiments to validate the effectiveness.

论文关键词：Personalized recommendation,Setwise ranking,Potential preference items

论文评审过程：Received 25 September 2021, Revised 26 April 2022, Accepted 27 April 2022, Available online 4 May 2022, Version of Record 14 May 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108946