Point of Interest Recommendation Methods in Location Based Social Networks: Traveling to a new geographical region
This addresses the challenge of providing personalized POI recommendations for users traveling to unfamiliar geographical areas, though it appears incremental as it builds on existing methods by incorporating additional data sources.
The paper tackles the problem of recommending points of interest (POIs) in location-based social networks when users move to new regions with little activity history, proposing a system based on user reviews and POI categories that achieves better accuracy on the Yelp dataset.
Recommender systems in location based social networks mainly take advantage of social and geographical influence in making personalized Points-of-interest (POI) recommendations. The social influence is obtained from social network friends or similar users based on matching visit history whilst the geographical influence is obtained from the geographical footprints users' leave when they check-in at different POIs. However, this approach may fall short when a user moves to a new region where they have little or no activity history. We propose a location aware POI recommendation system that models user preferences mainly based on; user reviews and categories of POIs. We evaluate our algorithm on the Yelp dataset and the experimental results show that our algorithm achieves a better accuracy.