Predicting POI Visits in a Heterogeneous Location-Based Social Network

A point of interest (POI) is a specific location that people may find useful or interesting, such as restaurants, stores, attractions, and hotels. With the recent proliferation of location-based social networks (LBSN), numerous users gather to interact and share information on various POIs. POI recommendations have become a crucial issue because it not only helps users to learn about new places but also gives LBSN providers chances to post POI advertisements. As we utilize a heterogeneous information network to represent an LBSN in this work, POI recommendations are remodeled as a link prediction problem, which is significant in the field of social network analysis. Moreover, we propose to utilize the meta-path-based approach to extract implicit but potentially useful relationships between a user and a POI. Then, the extracted topological features are used to construct a prediction model with appropriate data classification techniques. In our experiments, the Yelp dataset is utilized as our testbed for performance evaluation purposes. The results show that our prediction model is of good prediction quality in practical applications.