ST-RNet: A Time-aware Point-of-interest Recommendation Method based on Neural Network

Abstract

Point-of-interest (POI) recommendation is one of the most important services in the rapid growing location-based social networks (LBSNs). Good POI recommendation can help people explore the locations they haven't visited but are interested in, and help merchants find their target users. Time-aware POI recommendation aims to recommend unvisited POIs for a given user at a specified time in a day. However, previous methods, such as user-based collaborative filtering, lack the mining of the features of POIs and the learning of abstract spatio-temporal interactions. In this paper, we propose a novel time-aware POI recommendation method named ST-RNet (Spatio-Temporal Recommender Network) to address these shortages. ST-RNet works in the following fashion. Firstly, we analyze the crucial features in LBSNs to alleviate data sparsity problem and further measure the similarities between POIs. For subsequent network training, we then construct the embedding matrices with same dimension for users and POIs by POI-based Collaborative Filtering (PCF). Furthermore, the positive and negative check-in records are fed into a novel recommender neural network (RNet) to learn the embedding matrix of times and the abstract interactions between users, POIs and times. Finally, ST-RNet recommends the unvisited POIs most likely to be visited to a given user at a given time. The experimental results on Foursquare real-world dataset show that ST-RNet is effective on time-aware POI recommendation task and is capable of analyzing the hidden patterns behind spatio-temporal interactions.