short-paper

Atypical Queries in eCommerce

Authors:

Neeraj Pradhan,

Vinay Deolalikar,

Kang LiAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1767 - 1770

https://doi.org/10.1145/2806416.2806605

Published: 17 October 2015 Publication History

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Abstract

Understanding how specific, ambiguous, or broad the intent of a search query is, across all users of the system, is important in improving search relevance in eCommerce. There is scant literature on such a structural characterization of queries in eCommerce. In this paper, we use query-click log data to address the problem of identifying "atypical queries": these are queries that are extremal in terms of specificity, ambiguity, or breadth of intent. We isolate three components of atypicality: geometric, statistical, and topological. We demonstrate, using query-click logs at Groupon, that certain combinations of these properties render a query atypical, and discuss how search analysts treat such queries differently. Our work is being used to improve search relevance at Groupon.

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Cited By

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Pradhan NDeolalikar VSingh D(2018)Islands of Interest: Mining Concentrations of User Search Intent over e-Commerce Product Categories2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622155(3717-3722)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622155
Singh DDeolalikar V(2018)Automating Relevance Banding in e-Commerce Search using Click Model2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622113(3793-3797)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622113

Index Terms

Atypical Queries in eCommerce
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Information retrieval query processing

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Information

Published In

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2015

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

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Cited By

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Pradhan NDeolalikar VSingh D(2018)Islands of Interest: Mining Concentrations of User Search Intent over e-Commerce Product Categories2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622155(3717-3722)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622155
Singh DDeolalikar V(2018)Automating Relevance Banding in e-Commerce Search using Click Model2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622113(3793-3797)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622113

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