Point-of-Interest Recommendations: Learning Potential Check-ins from Friends
published: Sept. 27, 2016, recorded: August 2016, views: 1697
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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.
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