Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Measuring and Recommending Time-Sensitive Routes from Location-based Data / 4193
Hsun-Ping Hsieh, Cheng-Te Li, Shou-De Lin

Location-based services allow users to perform geo-spatial recording actions, which facilitates the mining of the moving activities of human beings. This paper proposes a system, TimeRouter, to recommend time-sensitive trip routes consisting of a sequence of locations with associated time stamps based on knowledge extracted from large-scale location check-in data. We first propose a statistical route goodness measure considering: (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. Then we construct the time-sensitive route recommender with two major functions: (1) constructing the route based on the user-specified source location with the starting time, (2) composing the route between the specified source location and the destination location given a starting time. We devise a search method, Guidance Search, to derive the routes efficiently and effectively. Experiments on Gowalla check-in datasets with user study show the promising performance of our proposed route recommendation method.