Point-of-Interest Recommendation for Users-Businesses with Uncertain Check-ins

Zhu Sun, Chen Li, Yu Lei, Lu Zhang, Jie Zhang, Shunpan Liang

Research output: Contribution to journalArticlepeer-review


Most existing studies on next point-of-interest (POI) recommendation assume that users deliver certain check-ins over individual POIs. In reality, we typically obtain uncertain check-ins due to the presence of collective POIs, which are gathering places of multiple individual POIs (e.g., shopping malls). On one hand, such uncertain check-ins over collective POIs hinder more accurate next POI recommendation for users due to the transition vanishing issue; on the other hand, the presence of collective POIs poses the challenge for businesses to select which collective POIs to locate in due to complicated competition and cooperation relations between businesses. As such, these collective POIs bring an unprecedented opportunity and necessity on recommendation for both users and businesses. Therefore, we propose novel solutions of location service beneficial for users-businesses. For users, we propose the STSP equipped with category- and location-aware encoders, to deliver more accurate next POI prediction by fusing rich context features. Regarding businesses, we explore their competition and cooperation relations from check-in records, based on which we derive the living environment (LE) of a business. Insight on site selection for businesses is provided by exploiting the LE, aiming to bring in more profits. Extensive empirical studies demonstrate the efficiency of our solutions.
Original languageEnglish
JournalIEEE Transactions on Knowledge and Data Engineering
Publication statusE-pub ahead of print - 22 Feb 2021


  • Business
  • Collective POIs
  • Correlation
  • Data analysis
  • Living Environment
  • Neural Networks
  • Next Point-of-Interest Recommendation
  • Social networking (online)
  • Sun
  • Uncertain Check-ins
  • Urban areas
  • Web search


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