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

研究成果: Conference contribution

10 引文 斯高帕斯(Scopus)

摘要

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).

原文English
主出版物標題WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
頁面529-530
頁數2
DOIs
出版狀態Published - 2012 5月 21
事件21st Annual Conference on World Wide Web, WWW'12 - Lyon, France
持續時間: 2012 4月 162012 4月 20

出版系列

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

Other

Other21st Annual Conference on World Wide Web, WWW'12
國家/地區France
城市Lyon
期間12-04-1612-04-20

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信

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