TripRec: recommending trip routes from large scale check-in data

Authors:
Hsun-Ping Hsieh;Cheng-Te Li;Shou-De Lin
Affiliations:
National Taiwan University, Taipei, Taiwan Roc;National Taiwan University, Taipei, Taiwan Roc;National Taiwan University, Taipei, Taiwan Roc
Venue:
Proceedings of the 21st international conference companion on World Wide Web
Year:
2012

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Abstract

With location-based services, such as Foursquare and Gowalla, users can easily perform check-in actions anywhere and anytime. Such check-in data not only enables personal geospatial journeys but also serves as a fine-grained source for trip planning. In this work, we aim to collectively recommend trip routes by leveraging a large-scaled check-in data through mining the moving behaviors of users. A novel recommendation system, TripRec, is proposed to allow users to pecify starting/end and must-go locations. It further provides the flexibility to satisfy certain time constraint (i.e., the expected duration of the trip). By considering a sequence of check-in points as a route, we mine the frequent sequences with some ranking mechanism to achieve the goal. Our TripRec targets at travelers who are unfamiliar to the objective area/city and have time constraints in the trip.