A robust personalized location recommendation based on ensemble learning

作者：

Highlights：

• We propose an ensemble-based personalized location recommendation algorithm.

• This paper considers both the accuracy and stability of recommender systems.

• Information gain is used as an evaluation index of system stability.

• The personal weights are individually calculated for each user.

摘要

•We propose an ensemble-based personalized location recommendation algorithm.•This paper considers both the accuracy and stability of recommender systems.•Information gain is used as an evaluation index of system stability.•The personal weights are individually calculated for each user.

论文关键词：Location-based social network,Recommender system,Location recommendation,Robustness,Ensemble learning,Information gain

论文评审过程：Received 2 May 2020, Revised 1 September 2020, Accepted 26 September 2020, Available online 6 October 2020, Version of Record 10 February 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114065