Personalized POI Recommendation In Location-Based Social Networks

Point-of-interest(POI)recommender system encourages users to share their locations and social experience through check-ins in online location-based social networks.A most recent algorithm for POI recommendation takes into account both the location relevance and diversity.The relevance measures users' personal preference while the diversity considers location categories.There exists a dilemma of weighting these two factors in the recommendation.The location diversity is weighted more when a user is new to a city and expects to explore the city in the new visit.In this paper,we propose a method to automatically adjust the weights according to user's personal preference.In general,the contributions in this paper can be summarized below:(1)we observe that users' desire to visit new POIs' categories is reduced over time.This exactly satisfies the law of diminishing marginal utility,which provides the direction for the subsequent research work.(2)We propose a method to automatically make a balance between the POI relevance and information coverage,focusing on investigating a function between the number of location categories and a weight value for each user.(3)We further improve the approximation by exploring similar behavior of users within a location category.(4)We conduct experiments on five real-world datasets,and show that the new approach can make a good balance of weighting the two factors therefore providing better recommendation.