MaP2R: A Personalized Maximum Probability Route Recommendation Method Using GPS Trajectories
Personalized travel route recommendation refers to the planning of an optimal travel route between two geographical locations based on the road networks and users’ travel preferences. In this paper, we extract users’ travel behaviours from their historical GPS trajectories and propose a personalized maximum probability route recommendation method called MaP2R. MaP2R utilizes the concepts of appearance behaviour and transition behaviour to describe users’ travel behaviours and applies matrix factorization and Laplace smoothing method to estimate users’ travel behaviour probabilities. When making recommendation, a route with the maximum probability of a user’s travel behaviours is generated based on Markov property and searched through a generated behaviour graph. The experimental results on a real GPS trajectory dataset show that the proposed MaP2R achieves better results for travel route recommendations compared with the existing methods.
KeywordsGPS trajectories Personalized travel route recommendation Collaborative filtering
The research is supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant to Xin Wang and National Natural Science Foundation of China (No. 41271387).
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