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In the environment of data explosion, how to make an effective and accurate personalized point of interest (POI) recommendation in location-based social networks (LBSNs) is a challenging and meaningful task. Fortunately, there is a lot of information that we can use. We can make recommendations by mining the rich information hidden in user check-in records. In this paper, we propose a recommend system named GFP-LORE. Specifically, we have designed a framework, which integrates various influencing factors. First, we modeled friend sign-in frequencies and POI popularity as a power-law distribution and the experiment proved that it is effective. Then, we got the influence of geographic information by modeling it according to the longitude and latitude of the user’s check-in location. After that, we sorted the historical check-in records of all users according to time and then mine an overall pattern of location transfer-order pattern. Then, we combine it with each user’s own unique location transfer record to get the possibility of the user going to the next POI. Finally, we synthesize the above four influence factors into a unified correlation probability rating and recommend a new location by this probability rating. We tested our system on the open real check-in data set, and the results of our simulation experiments show that the recommendation effect of our system is better than those algorithms used in the contrast test.

A Multi-Element Hybrid Location Recommendation Algorithm for Location Based Social Networks