Point of Interest Recommendation Methods in Location Based Social Networks: Traveling to a new geographical region

TL;DR

This paper introduces a location-aware POI recommendation system that effectively predicts user preferences in new regions by leveraging reviews and POI categories, outperforming traditional social and geographical influence methods.

Contribution

The paper presents a novel POI recommendation approach that focuses on reviews and categories, addressing the cold-start problem in new geographical regions.

Findings

Improved accuracy over existing methods

Effective in regions with little user activity history

Validated on Yelp dataset

Abstract

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.