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

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. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a two-stage coarse-to-fine POI recommendation algorithm based on tensor factorization and weighted distance kernel density estimation. The method takes into account long-term preferences and crowd preferences to estimate user interests, and considers spatial distance to determine fine-grained user-location interests.