Location recommendation by combining geographical, categorical, and social preferences with location popularity
作者：
Highlights：
• A unified location recommendation framework is proposed to measure users’ check-in probability of unvisited locations.
• A novel method based on category hierarchy and semantic similarity between location tags is proposed to model categorical preference.
• Comprehensive experiments are conducted to show that our method outperforms other state-of-the-art baselines.
• Location popularity, exploited as a global attribute of locations, can result in a significant improvement on recommendation performance.
• More friends do little help in improving the precision.
摘要
•A unified location recommendation framework is proposed to measure users’ check-in probability of unvisited locations.•A novel method based on category hierarchy and semantic similarity between location tags is proposed to model categorical preference.•Comprehensive experiments are conducted to show that our method outperforms other state-of-the-art baselines.•Location popularity, exploited as a global attribute of locations, can result in a significant improvement on recommendation performance.•More friends do little help in improving the precision.
论文关键词：Location recommendation,Check-in,Hybrid method,Categorical hierarchy,Social correlation,Location popularity
论文评审过程：Received 21 June 2019, Revised 1 March 2020, Accepted 19 March 2020, Available online 30 March 2020, Version of Record 30 March 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102251
