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10.1145/2187980.2188091acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
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Mining for insights in the search engine query stream

Published: 16 April 2012 Publication History

Abstract

Search engines record a large amount of metadata each time a user issues a query. While efficiently mining this data can be challeng-ing, the results can be useful in multiple ways, including monitoring search engine performance, improving search relevance, prioritizing research, and optimizing day-to-day operations. In this poster, we describe an approach for mining query log data for actionable insights - specific query segments (sets of queries) that require attention, and actions that need to be taken to improve the segments. Starting with a set of important metrics, we identify query segments that are "interesting" with respect to these metrics using a distributed frequent itemset mining algorithm.

References

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Chaiken, R., Jenkins, B., Larson, P.-R., Ramsey, B., Shakib, D., Weaver, S., and Zhou, J. (2008). SCOPE: easy and efficient parallel processing of massive data sets. Proc. VLDB Endow., 1(2): 1265--1276.
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Han, J., Pei, J., Yin, Y. and Mao, R. (2004). Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Disc., 8(1): 53--87.
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Li, H., Wang, Y., Zhang, D., Zhang, M., and Chang, E.Y. (2008), PFP: parallel FP-growth for query recommendation. Proc. RecSys, 107--114.
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Tan, B. and Peng, F. (2008). Unsupervised query segmentation using generative language models and wikipedia. Proc. WWW, 347--356.
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Wang, K., Walker, T., and Zheng, Z. (2008). PSkip: estimating relevance ranking quality from web search clickthrough data. Proc. SIGKDD, 1355--1364

Cited By

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  • (2018)Efficient Query Suggestion System using Users Search History2018 International Conference on Information , Communication, Engineering and Technology (ICICET)10.1109/ICICET.2018.8533799(1-6)Online publication date: Aug-2018
  • (2017)Real-time detection of navigation problems on the World Wild WebInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2016.12.002101:C(1-9)Online publication date: 1-May-2017
  • (2016)Measuring and Predicting Search Engine Users’ SatisfactionACM Computing Surveys10.1145/289348649:1(1-35)Online publication date: 28-Jul-2016
  • Show More Cited By

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  1. Mining for insights in the search engine query stream

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    Published In

    cover image ACM Other conferences
    WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web
    April 2012
    1250 pages
    ISBN:9781450312301
    DOI:10.1145/2187980

    Sponsors

    • Univ. de Lyon: Universite de Lyon

    In-Cooperation

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 April 2012

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    Author Tags

    1. fp-growth
    2. frequent itemset mining
    3. query log analysis

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    • Poster

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    WWW 2012
    Sponsor:
    • Univ. de Lyon
    WWW 2012: 21st World Wide Web Conference 2012
    April 16 - 20, 2012
    Lyon, France

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    Cited By

    View all
    • (2018)Efficient Query Suggestion System using Users Search History2018 International Conference on Information , Communication, Engineering and Technology (ICICET)10.1109/ICICET.2018.8533799(1-6)Online publication date: Aug-2018
    • (2017)Real-time detection of navigation problems on the World Wild WebInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2016.12.002101:C(1-9)Online publication date: 1-May-2017
    • (2016)Measuring and Predicting Search Engine Users’ SatisfactionACM Computing Surveys10.1145/289348649:1(1-35)Online publication date: 28-Jul-2016
    • (2013)Toward self-correcting search enginesProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484043(263-272)Online publication date: 28-Jul-2013

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