Extracting search query patterns via the pairwise coupled topic model

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

4 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
出版社Association for Computing Machinery, Inc
ページ655-664
ページ数10
ISBN(電子版)9781450337168
DOI
出版ステータス出版済み - 2 8 2016
外部発表はい
イベント9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, 米国
継続期間: 2 22 20162 25 2016

出版物シリーズ

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

会議

会議9th ACM International Conference on Web Search and Data Mining, WSDM 2016
国/地域米国
CitySan Francisco
Period2/22/162/25/16

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンスの応用
  • ソフトウェア
  • コンピュータ ネットワークおよび通信

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