A self-adaptive point-of-interest recommendation algorithm based on a multi-order Markov model

As one of the personalization technologies, point-of-interest (POI) recommendation systems have attracted more and more attention from academic and industrial researchers. Exploiting the spatio-temporal pattern of users check-ins for user modeling is the core content of the current research of POI recommendation in location-based social networks (LBSNs). In this paper, we propose a POI recommendation algorithm based on a multi-order Markov model, which predicts users next favorite POIs based not only on their current location but also on their previous location, and propose a self-adaptive algorithm to adjust our multi-order Markov model to be available to all users check-ins. Moreover, to improve the precision of our proposed POI recommendation algorithm, we incorporate the geographical influence and temporal popularity of users checked-in POIs into our proposed algorithm. Finally, experimental results on two real datasets demonstrate that our proposed algorithm outperforms the state-of-the-art POI recommendation methods in terms of F−measure@N(N=5,10,15,20).