Recommendation Taking into Account the Time Aspects of Users and Items

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 consider their seasonality and long-term trends. In contrast to existing methods, we model these temporal aspects specifically for individual geographical areas instead of globally. In addition, a geographical post-filter method is used for creating personal regions of users. The preliminary results show that consideration of locality-specific seasonality and long-term trends in categories’ popularity can improve the performance of the proposed recommender system.