Extracting search query patterns via the pairwise coupled topic model

Takuya Konishi, Takuya Ohwa, Sumio Fujita, Kazushi Ikeda, Kohei Hayashi

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

4 Citations (Scopus)

Abstract

A fundamental yet new challenge in information retrieval is the identification of patterns behind search queries. For example, the query "NY restaurant" and "boston hotel" shares the common pattern "LOCATION SERVICE". However, because of the diversity of real queries, existing approaches require data preprocessing by humans or specifying the target query domains, which hinders their applicability. We propose a probabilistic topic model that assumes that each term (e.g., "NY") has a topic (LOCATION). The key idea is that we consider topic co-occurrence in a query rather than a topic sequence, which significantly reduces computational cost yet enables us to acquire coherent topics without the preprocessing. Using two real query datasets, we demonstrate that the obtained topics are intelligible by humans, and are highly accurate in keyword prediction and query generation tasks.

Original languageEnglish
Title of host publicationWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages655-664
Number of pages10
ISBN (Electronic)9781450337168
DOIs
Publication statusPublished - Feb 8 2016
Externally publishedYes
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

Conference

Conference9th ACM International Conference on Web Search and Data Mining, WSDM 2016
CountryUnited States
CitySan Francisco
Period2/22/162/25/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software
  • Computer Networks and Communications

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