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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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).

Original languageEnglish
Title of host publicationWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
Pages529-530
Number of pages2
DOIs
Publication statusPublished - 2012 May 21
Event21st Annual Conference on World Wide Web, WWW'12 - Lyon, France
Duration: 2012 Apr 162012 Apr 20

Publication series

NameWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion

Other

Other21st Annual Conference on World Wide Web, WWW'12
CountryFrance
CityLyon
Period12-04-1612-04-20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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