Points-of-interest recommendation based on convolution matrix factorization

A point-of-interest(POI) recommendation aims to mine a user’s visiting history and find her/his potentially preferred places. The decision process when choosing a POI is complex and can be influenced by numerous factors, including personal preferences, geographical considerations, and user social relations. While latent factor models have been proven effective and are widely used for recommendations, adopting them to POI recommendations requires delicate consideration of the unique characteristics of location-based social networks (LBSNs). To this end, in this paper, we propose a joint convolution matrix factorization model, named the Review Geographical Social (ReGS) which strategically takes various factors into consideration. Specifically, this model captures geographical influences from a user’s check-in behaviour, and user social relations can be effectively leveraged in the recommendation model. The reviews information available on LBSNs could be related to a user’s check-in action, providing a unique opportunity for a POI recommendation. We model above three types of information under a unified POI recommendation framework based on convolution matrix factorization which integrates a convolutional neural network into a probability matrix factorization. Finally, we conduct a comprehensive performance evaluation for the ReGS using two real-world datasets collected from Foursquare. Experimental results show that the ReGS achieves significantly superior precision and recall rates to other state-of-the-art recommendation models.