Adaptive location recommendation algorithm based on location-based social networks

With the development of social network and location-based services, location-based social network rose. In the Geo-Social recommended system, location recommendation has become a focus of recent research. This paper analyzes three questions the personalized recommendation algorithm may face: location data sparseness, cold start and registered locations near and far from the usual residence. Through the analysis of those questions, we propose an improved adaptive location recommendation algorithm. This algorithm merges user collaborative filtering, social influence, and naive Bayesian classification. It adapts to the user´s current location, and recommend the most suitable location. In this paper, we compare the improved algorithm with other recommendation algorithms, verifying the feasibility, and effectiveness of the improved algorithm. Experimental results indicate that the improved algorithm can solve the problems of personalized place recommendations, and recommend place better.