Aim of this paper is to propose a user-centric location recommendation service for the rapidly increasing LBSN (location-based social network). Our idea is to consider three important influencing factors i.e. client predilection, social impacts and distance influence for point-of-interest recommendations. Also, the influence factors i.e. client predilection, social impact are predicted via user-based collaborative filtering and friend-based collaborative filtering, we propose a technique to focus more on distance factor impacts because of the spatial clustering recorded in user visiting locations in LBSNs. Our research shows that the distance influence among locations plays a vital role in user check-in practices which is implemented by power law distribution. Likewise, we build an agglomerative location recommendation system, which combines client predilection to a location with social effect and distance influence. Our result shows that the proposed fusion framework performs better than the already proposed recommendation techniques.

LBSN,point-of-interest recommendation system,power-law

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