Learning search tasks in queries and web pages via graph regularization

Ming Ji, Jun Yan, Siyu Gu, Jiawei Han, Xiaofei He, Wei Vivian Zhang, Zheng Chen

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

Abstract

As the Internet grows explosively, search engines play a more and more important role for users in effectively accessing online information. Recently, it has been recognized that a query is often triggered by a search task that the user wants to accomplish. Similarly, many web pages are specifically designed to help accomplish a certain task. Therefore, learning hidden tasks behind queries and web pages can help search engines return the most useful web pages to users by task matching. For instance, the search task that triggers query "thinkpad T410 broken" is to maintain a computer, and it is desirable for a search engine to return the Lenovo troubleshooting page on the top of the list. However, existing search engine technologies mainly focus on topic detection or relevance ranking, which are not able to predict the task that triggers a query and the task a web page can accomplish. In this paper, we propose to simultaneously classify queries and web pages into the popular search tasks by exploiting their content together with click-through logs. Specifically, we construct a task-oriented heterogeneous graph among queries and web pages. Each pair of objects in the graph are linked together as long as they potentially share similar search tasks. A novel graph-based regularization algorithm is designed for search task prediction by leveraging the graph. Extensive experiments in real search log data demonstrate the effectiveness of our method over state-of-the-art classifiers, and the search performance can be significantly improved by using the task prediction results as additional information.

Original languageEnglish (US)
Title of host publicationSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages55-64
Number of pages10
ISBN (Print)9781450309349
DOIs
StatePublished - Jan 1 2011
Event34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011 - Beijing, China
Duration: Jul 24 2011Jul 28 2011

Publication series

NameSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011
CountryChina
CityBeijing
Period7/24/117/28/11

Keywords

  • Classification
  • Graph regularization
  • Web search task

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Ji, M., Yan, J., Gu, S., Han, J., He, X., Zhang, W. V., & Chen, Z. (2011). Learning search tasks in queries and web pages via graph regularization. In SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 55-64). (SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery. https://doi.org/10.1145/2009916.2009928