Trajectory-based point of interest recommendation

Existing point of interest (POI) recommendation systems for mobile users only consider a user's present spatio-temporal location, and do not utilize a user's trajectory history. In this thesis, we identify some essential requirements for a mobile trajectory-based recommendation system, and present a new framework for trajectory-based POI recommendation. We construct a k-truncated generalized suffix tree to represent a historical trajectory database, and use it to execute exact matching recommendation queries. In addition to individual points of interest, we can recommend generalizations of POIs by using density estimation. We also consider extensions of our framework. Two variants are developed, allowing for the execution of fuzzy matching and order-flexible queries. Furthermore, a technique for diversifying recommendations is presented. The resulting system can efficiently and accurately predict a user's next visited point given a query, and is demonstrated to be effective and scalable on two real world datasets.