A Hybrid Scheme for Spatio-Textual Recommender System

Location Based Social Networks (LBSNs) enable their user to share their check-ins and post reviews about them. The availability of spatial and textual information in LBSNs offers an opportunity to explore user's history and preferences to find the locations that the user might be interested in. Point-Of-Interests (POIs) spatial features are one of the most important data available on LBSNs as it has a huge impact on user's choice of new location to visit. Users' reviews and POIs' categories are another valuable resources of information in LBSNs which help infer users' interest and POIs' features. Recent researches attempt to improve the performance of POI recommendation models by making use of different information sources available in social network. In this paper, we examine the impact of using this information on the accuracy of recommendation task. Our major contribution is proposing the model which use heterogeneous context information in the form of a weighted linear combination. We argue that the weights of this combination should be learned for each user separately instead of using the same set of weights for all users. We provide an algorithm for learning the weights for each user such that recommendation accuracy is improved. In addition, it is enable to incorporate extra information source to our proposed model without requirement of changing the model completely or adding extra complexity to it. Experiments conducted on two large datasets of real world, Yelp and Foursquare, shows the effectiveness of the proposed method.