Exploiting large-scale check-in data to recommend time-sensitive routes

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

53 Citations (Scopus)

Abstract

Location-based services allow users to perform geo-spatial check-in actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale check-in data. Given a query location with the starting time, our goal is to recommend a time-sensitive route. We argue a good route should consider (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. By devising a statistical model, we integrate these four factors into a goodness function which aims to measure the quality of a route. Equipped with the goodness measure, we propose a greedy method to construct the time-sensitive route for the query. Experiments on Gowalla datasets demonstrate the effectiveness of our model on detecting real routes and cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.

Original languageEnglish
Title of host publicationInternational Workshop on Urban Computing, UrbComp 2012 - Held in Conjunction with KDD 2012
Pages55-62
Number of pages8
DOIs
Publication statusPublished - 2012 Sep 14
EventInternational Workshop on Urban Computing, UrbComp 2012 - Held in Conjunctionwith KDD 2012 - Beijing, China
Duration: 2012 Aug 122012 Aug 12

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

OtherInternational Workshop on Urban Computing, UrbComp 2012 - Held in Conjunctionwith KDD 2012
CountryChina
CityBeijing
Period12-08-1212-08-12

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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  • Cite this

    Hsieh, H. P., Li, C. T., & Lin, S. D. (2012). Exploiting large-scale check-in data to recommend time-sensitive routes. In International Workshop on Urban Computing, UrbComp 2012 - Held in Conjunction with KDD 2012 (pp. 55-62). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2346496.2346506