Point-of-Interest Recommendations in Location-based Social Networks
With the rapid development of Location-based Social Network (LBSN) services, a large number of Point-Of-Interests (POIs) have been available, which consequently raises a great demand of building personalized POI recommender systems. A personalized POI recommender system can significantly assist users to find their preferred POIs and help POI owners to attract more customers. However, it is very challenging to develop a personalized POI recommender system because a user's check-in decision making process is very complex and could be influenced by many factors such as social network, geographical position, and the dynamics of user preferences. In the first part of this talk, we propose to divide the whole recommendation space into two parts: social friend space and user interest space, and we develop models for each space for generating recommendations. In the second part of this talk, we introduce a new ranked based method for implicit feedback-based recommendation. To evaluate the proposed methods, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on the real-world data sets.
(Refreshments will be served.)