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Learning to detect task boundaries of query session

Published: 27 October 2013 Publication History

Abstract

To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement.

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  1. Learning to detect task boundaries of query session

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    cover image ACM Conferences
    CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
    October 2013
    2612 pages
    ISBN:9781450322638
    DOI:10.1145/2505515
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 27 October 2013

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    Author Tags

    1. query task session
    2. search task
    3. task boundary detection

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    CIKM'13: 22nd ACM International Conference on Information and Knowledge Management
    October 27 - November 1, 2013
    California, San Francisco, USA

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    CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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