Modeling check-in preferences with multidimensional knowledge: A minimax entropy approach

Jingjing Wang, Min Li, Jiawei Han, Xiaolong Wang

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

Abstract

We propose a single unified minimax entropy approach for user preference modeling with multidimensional knowledge. Our approach provides a discriminative learning protocol which is able to simultaneously a) leverage explicit human knowledge, which are encoded as explicit features, and b) model the more ambiguous hidden intent, which are encoded as latent features. A latent feature can be carved by any parametric form, which allows it to accommodate arbitrary underlying assumptions. We present our approach in the scenario of check-in preference learning and demonstrate it is capable of modeling user preference in an optimized manner. Check-in preference is a fundamental component of Point-of-Interest (POI) prediction and recommendation. A user's check-in can be affected at multiple dimensions, such as the particular time, popularity of the place, his/her category and geographic preference, etc. With the geographic preferences modeled as latent features and the rest as explicit features, our approach provides an in-depth understanding of users' time-varying preferences over different POIs, as well as a reasonable representation of the hidden geographic clusters in a joint manner. Experimental results based on the task of POI prediction/recommendation with two real-world checkin datasets demonstrate that our approach can accurately model the check-in preferences and significantly outperforms the state-of-art models.

Original languageEnglish (US)
Title of host publicationWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages297-306
Number of pages10
ISBN (Electronic)9781450337168
DOIs
StatePublished - Feb 8 2016
Event9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, United States
Duration: Feb 22 2016Feb 25 2016

Publication series

NameWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining

Other

Other9th ACM International Conference on Web Search and Data Mining, WSDM 2016
CountryUnited States
CitySan Francisco
Period2/22/162/25/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computer Networks and Communications

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