Abstract
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.
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Acknowledgments
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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Stefancova, E., Srba, I. (2020). POI Recommendation Based on Locality-Specific Seasonality and Long-Term Trends. In: Chatzigeorgiou, A., et al. SOFSEM 2020: Theory and Practice of Computer Science. SOFSEM 2020. Lecture Notes in Computer Science(), vol 12011. Springer, Cham. https://doi.org/10.1007/978-3-030-38919-2_28
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DOI: https://doi.org/10.1007/978-3-030-38919-2_28
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