The focus of point-of-interest recommendation techniques is to suggest a venue to a given user that would
match the users’ interests and is likely to be adopted by the user. Given the multitude of the available venues and the sparsity of user check-ins, the problem of recommending venues has shown to be a difficult task. Existing literature has already explored various types of features such as geographical distribution, social structure and temporal behavioral patterns to make a recommendation. In this thesis, we show how a comprehensive set of user and venue related information can be methodically incorporated into a heterogeneous graph representation based on which the problem of venue recommendation can be efficiently formulated as an instance of the heterogeneous link prediction problem on the graph and we propose a new set of features derived based on the neural embeddings of venues and users. We
additionally show how the neural embeddings for users and venues can be jointly learnt based on the prior check-in sequence of users and then be used to define a set of new features. We have also used a new proposed heterogeneous graph similarity search framework to find similarity between users and venues using our graph. These features are integrated into a feature-based matrix factorization model. Our experiments show that the features defined over the user and venue embeddings are effective for venue recommendation and outperform existing state of the art methods.
Point-of-interest recommendation in location-based social networks
