A coarse-to-fine user preferences prediction method for point-of-interest recommendation

作者：Cai, Liangqi; Wen, Wen; Wu, Biao; Yang, Xiaowei^*

来源：NEUROCOMPUTING, 2021, 422: 1-11.

DOI：10.1016/j.neucom.2020.09.034

• Coarse-to-fine
• Long-term preferences
• Crowd's influences
• Tensor factorization
• Point-of-interest

Point-of-interests (POIs) recommendations aim at recommending locations to users on social platforms by analyzing their histories or combining other information. At present, the different granularity of fac-tors (i.e. time, geography and sociability) are not thoroughly studied in existing works. To deal with this problem, we propose a two-stage coarse-to-fine POI recommendation algorithm based on tensor factorization and weighted distance kernel density estimation (KDE). At first stage, we take account of not only long-term preferences with sequential context, but also the crowd's preferences to estimate the coarse user-category interest. And then a specific-designed weighted KDE with consideration of spatial distance is employed to determine the fine-grained user-location interest. To evaluate the proposed method, experiments are conducted on two real benchmark location-based social network (LBSN) datasets. And the results show that the proposed method outperforms the state-of-the-art methods and produces better POI recommendation.

• 单位
  广东工业大学