Point of Interest Recommendations: Learning Potential Check ins from Friends
The emergence of Location based Social Network (LBSN) services provides a wonderful opportunity to build personalized Point of Interest (POI) recommender systems. Although a personalized POI recommender system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model user's POI decision making process and the difficulty to address data sparsity and user/location cold start problem. To cope with these challenges, we define three types of friends (i.e. social friends, location friends, and neighboring friends) in LBSN, and develop a two step framework to leverage the information of friends to improve POI recommendation accuracy and address cold start problem. Specifically, we first propose to learn a set of potential locations that each individual's friends have checked in before and this individual is most interested in. Then we incorporate three types of check ins (i.e. observed check ins, potential check ins and other unobserved check ins) into matrix factorization model using two different loss functions (i.e. the square error based loss and the ranking error based loss) To evaluate the proposed model, we conduct extensive experiments with many state of the art baseline methods and evaluation metrics on two real world data sets. The experimental results demonstrate the effectiveness of our methods.
KDD