Dynamic query intent mining from a search log stream

Yanan Qian, Tetsuya Sakai, Junting Ye, Qinghua Zheng, Cong Li

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

13 Citations (Scopus)

Abstract

It has long been recognized that search queries are often broad and ambiguous. Even when submitting the same query, different users may have different search intents. Moreover, the intents are dynamically evolving. Some intents are constantly popular with users, others are more bursty. We propose a method for mining dynamic query intents from search query logs. By regarding the query logs as a data stream, we identify constant intents while quickly capturing new bursty intents. To evaluate the accuracy and efficiency of our method, we conducted experiments using 50 topics from the NTCIR INTENT-9 data and additional five popular topics, all supplemented with six-month query logs from a commercial search engine. Our results show that our method can accurately capture new intents with short response time.

Original languageEnglish
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages1205-1208
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 11
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: 2013 Oct 272013 Nov 1

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period13/10/2713/11/1

Keywords

  • Intent mining
  • Search query log
  • Stream data mining

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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  • Cite this

    Qian, Y., Sakai, T., Ye, J., Zheng, Q., & Li, C. (2013). Dynamic query intent mining from a search log stream. In CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (pp. 1205-1208). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2505515.2507856