GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-Based Social Networks

In this study we investigate the recommendation problem with multifaceted contextual information to overcome the scarcity of users' check-in data in Location-based Social Networks. To generate accurate personalized Point-of-Interest (POI) recommendations in the presence of data scarcity, we account for both users' and POIs' contextual information such as the social influence of friends, as well as the geographical and sequential transition influence of POIs on user's check-in behavior. We first propose a multi-view learning strategy to capture the multifaceted contextual information of users and POIs along with users' check-in data. Then, we feed the learned user and POI latent vectors to a deep neural framework, to capture their non-linear correlations. Finally, we formulate the objective function of our geo-based deep collaborative filtering model (GeoDCF) as a Bayesian personalized ranking problem to focus on the top-k recommendation task and we learn the parameters of our model via backpropagation. Our experiments on real-world datasets confirm that GeoDCF achieves high recommendation accuracy, significantly outperforming other state-of-the-art methods. Furthermore, we confirm the influence of both users' and POIs' contextual information on our GeoDCF model. The evaluation datasets are publicly available at: http://snap.stanford.edu/data/loc-gowalla.html, https://sites.google.com/site/yangdingqi/home/foursquare-dataset.