Inferring visiting time distributions of locations from incomplete check-in data

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

1 Citation (Scopus)

Abstract

Online location-based services, such as Foursquare and Facebook, provide a great resource for location recommendation. As we know the time is one of the important factors on recommending places with proper time for users, since the pleasure of visiting a place could be diminished if arriving at wrong time, we propose to infer the visiting time distributions of locations. We assume the check-in data used is incomplete because in real-world scenarios it is hard or unavailable to collect all the temporal information of locations and the check-in behaviors might be abnormal. To tackle such problem, we devise a visiting time inference framework, VisTime-Miner, which considers the route-based visiting correlation of time labels to model the visiting behavior of a location. Experiments on a large-scaled Gowalla check-in data show a promising result.

Original languageEnglish
Title of host publicationWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages295-296
Number of pages2
ISBN (Electronic)9781450327459
DOIs
Publication statusPublished - 2014 Apr 7
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: 2014 Apr 72014 Apr 11

Publication series

NameWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web

Other

Other23rd International Conference on World Wide Web, WWW 2014
CountryKorea, Republic of
CitySeoul
Period14-04-0714-04-11

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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  • Cite this

    Hsieh, H. P., & Li, C. T. (2014). Inferring visiting time distributions of locations from incomplete check-in data. In WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web (pp. 295-296). (WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web). Association for Computing Machinery, Inc. https://doi.org/10.1145/2567948.2577362