User modeling in search logs via a nonparametric Bayesian approach

Hongning Wang, Cheng Xiang Zhai, Feng Liang, Anlei Dong, Yi Chang

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

Abstract

Searchers' information needs are diverse and cover a broad range of topics; hence, it is important for search engines to accurately understand each individual user's search intents in order to provide optimal search results. Search log data, which records users' search behaviors when interacting with search engines, provides a valuable source of information about users' search intents. Therefore, properly characterizing the heterogeneity among the users' observed search behaviors is the key to accurately understanding their search intents and to further predicting their behaviors. In this work, we study the problem of user modeling in the search log data and propose a generative model, dpRank, within a non-parametric Bayesian framework. By postulating generative assumptions about a user's search behaviors, dpRank identifies each individual user's latent search interests and his/her distinct result preferences in a joint manner. Experimental results on a large-scale news search log data set validate the effectiveness of the proposed approach, which not only provides in-depth understanding of a user's search intents but also benefits a variety of personalized applications.

Original languageEnglish (US)
Title of host publicationWSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery
Pages203-212
Number of pages10
ISBN (Print)9781450323512
DOIs
StatePublished - 2014
Event7th ACM International Conference on Web Search and Data Mining, WSDM 2014 - New York, NY, United States
Duration: Feb 24 2014Feb 28 2014

Publication series

NameWSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining

Other

Other7th ACM International Conference on Web Search and Data Mining, WSDM 2014
CountryUnited States
CityNew York, NY
Period2/24/142/28/14

Keywords

  • non-parametric bayesian
  • search log mining
  • user modeling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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