TripRec: recommending trip routes from large scale check-in data.
Journal
Proceedings of the 21st World Wide Web Conference, WWW 2012, Lyon, France, April 16-20, 2012 (Companion Volume)
Pages
529-530
Date Issued
2012
Author(s)
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. Copyright is held by the author/owner(s).
SDGs
Type
conference paper