skip to main content
10.1145/2187980.2187989acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

Rewriting null e-commerce queries to recommend products

Published: 16 April 2012 Publication History

Abstract

In e-commerce applications product descriptions are often concise. E-Commerce search engines often have to deal with queries that cannot be easily matched to product inventory resulting in zero recall or null query situations. Null queries arise from differences in buyer and seller vocabulary or from the transient nature of products. In this paper, we describe a system that rewrites null e-commerce queries to find matching products as close to the original query as possible. The system uses query relaxation to rewrite null queries in order to match products. Using eBay as an example of a dynamic marketplace, we show how using temporal feedback that respects product category structure using the repository of expired products, we improve the quality of recommended results. The system is scalable and can be run in a high volume setting. We show through our experiments that high quality product recommendations for more than 25% of null queries are achievable.

References

[1]
I. Antonellis, H. Garcia-Molina, and C.-C. Chang. Simrank+: query rewriting through link analysis of the clickgraph (poster). In WWW, pages 408--421, 2008.
[2]
M. Bendersky and W. B. Croft. Discovering key concepts in verbose queries. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 491--498, 2008.
[3]
M. Bendersky and W. B. Croft. Analysis of long queries in a large scale search log. In WSCD, pages 8--14, 2009.
[4]
A. Z. Broder, M. Fontoura, E. Gabrilovich, A. Joshi, V. Josifovski, and T. Zhang. Robust classification of rare queries using web knowledge. In Proceedings of SIGIR, pages 231--238, 2007.
[5]
S. Datta and V. Varma. Tossing coins to trim long queries. In SIGIR, pages 1255--1256, 2011.
[6]
M. Efron and G. Golovchinsky. Estimation methods for ranking recent information. In Proceedings of SIGIR, pages 495--504, 2011.
[7]
M. Fontoura, V. Josifovski, R. Kumar, C. Olston, A. Tomkins, and S. Vassilvitskii. Relaxation in text search using taxonomies. In Proc. VLDB Endow., volume 1, pages 672--683, 2008.
[8]
S. Gollapudi, S. Ieong, A. Ntoulas, and S. Paparizos. Efficient query rewrite for structured web queries. In Proceedings of CIKM, pages 2417--2420, 2011.
[9]
D. Harman. Relevance feedback revisited. In Proceedings of SIGIR, pages 1--10, 1992.
[10]
M. A. Hasan, N. Parikh, G. Singh, and N. Sundaresan. Query suggestion for e-commerce sites. In Proceedings of the WSDM, 2011.
[11]
Y. hen and J. F. Canny. Recommending ephemeral items at web scale. In Proceedings of SIGIR, pages 1013--1022, 2011.
[12]
R. JJ. Relevance feedback in information retrieval. In Salton G. (Ed.), The SMART Retrieval System, pages 313--323, 1971.
[13]
R. Jones and D. C. Fain. Query word deletion prediction. In SIGIR, pages 435--436, 2003.
[14]
R. Jones, B. Rey, O. Madani, and W. Greiner. Generating query substitutions. In Proceedings of WWW, pages 387--396, 2006.
[15]
M. Keikha, S. Gerani, and F. Crestani. Temper: A temporal relevance feedback method. In ECIR, pages 436--447, 2011.
[16]
G. Kumaran and V. R. Carvalho. Reducing long queries using query quality predictors. In SIGIR, pages 564--571, 2009.
[17]
A. Malekian, C.-C. Chang, R. Kumar, and G. Wang. Optimizing query rewrites for keyword-based advertising. In Proceedings of the 9th ACM conference on Electronic commerce, pages 10--19, 2008.
[18]
N. Parikh and N. Sundaresan. Inferring semantic query relations from collective user behavior. In Proceedings of CIKM, pages 349--358, 2008.
[19]
N. Parikh and N. Sundaresan. Buzz-based recommender system. In Proceedings of WWW, 2009.
[20]
M. S. Pera. Improving library searches using word-correlation factors and folksonomies. In Master of Science Thesis, Department of Computer Science, Brigham Young University, 2009.
[21]
G. Singh, N. Parikh, and N. Sundaresn. User reaction to zero-recall ecommerce queries. In SIGIR, pages 75--84, 2011.
[22]
N. Sundaresan. Recommender systems at the long tail. In RecSys, pages 1--6, 2011.
[23]
K. Toutanova. Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In EMNLP/VLC, 2000.
[24]
X. Xue and W. B. Croft. Modeling subset distributions for verbose queries. In SIGIR, pages 1133--1134, 2011.
[25]
X. Xue, S. Huston, and W. B. Croft. Improving verbose queries using subset distribution. In CIKM, pages 1059--1068, 2010.
[26]
L. Zhao and J. Callan. Term necessity prediction. In CIKM, pages 259--268, 2010.

Cited By

View all

Index Terms

  1. Rewriting null e-commerce queries to recommend products

    Recommendations

    Comments

    Information & Contributors

    Information

    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
    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 ACM 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]

    Sponsors

    • Univ. de Lyon: Universite de Lyon

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 April 2012

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. e-commerce search
    2. null query
    3. query rewrite
    4. temporal feedback
    5. zero recall

    Qualifiers

    • Research-article

    Conference

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

    Acceptance Rates

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

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)16
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 06 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media