POI Recommendation Based on Locality-Specific Seasonality and Long-Term Trends

  • Elena StefancovaEmail author
  • Ivan Srba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12011)


This work deals with time-aware recommender systems in a domain of location-based social networks, such as Yelp or Foursquare. We propose a novel method to recommend Points of Interest (POIs) which considers their yearly seasonality and long-term trends. In contrast to the existing methods, we model these temporal aspects specifically for individual geographical localities instead of globally. According to the results achieved by the experimental evaluation on Yelp dataset, locality-specific seasonality can significantly improve the recommendation performance in comparison to its global alternative. We found out that it is helpful mostly within recommendations for highly-active users (it has a smaller influence for the novice users) and as expected, in localities with a strong seasonal weather variation. Another interesting finding is that in contrast to seasonality, we did not observe an improvement in case of locality-specific long-term trends.


Recommender systems Points of interest Time-aware recommendation Seasonality Long-term trends 



This work was partially supported by the Slovak Research and Development Agency under the contracts No. APVV-15-0508 and APVV SK-IL-RD-18-0004, by the Scientific Grant Agency of the Slovak Republic under the contracts No. VG 1/0667/18 and VG 1/0725/19, and by the student grant provided by Softec Pro Society.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Slovak University of Technology in BratislavaBratislavaSlovakia

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