research-article

Result enrichment in commerce search using browse trails

Authors:

Debmalya Panigrahi,

Sreenivas GollapudiAuthors Info & Claims

WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

Pages 267 - 276

https://doi.org/10.1145/1935826.1935874

Published: 09 February 2011 Publication History

Abstract

Commerce search engines have become popular in recent years, as users increasingly search for (and buy) products on the web. In response to an user query, they surface links to products in their catalog (or index) that match the requirements specified in the query. Often, few or no product in the catalog matches the user query exactly, and the search engine is forced to return a set of products that partially match the query. This paper considers the problem of choosing a set of products in response to an user query, so as to ensure maximum user satisfaction. We call this the result enrichment problem in commerce search.

The challenge in result enrichment is two-fold: the search engine needs to estimate the extent to which a user genuinely cares about an attribute that she has specified in a query; then, it must display products in the catalog that match the user requirement on the important attributes, but have a similar but possibly non-identical value on the less important ones. To this end, we propose a technique for measuring the importance of individual attribute values and the similarity between different values of an attribute. A novelty of our approach is that we use entire browse trails, rather than just clickthrough rates, in this estimation algorithm. We develop a model for this problem, propose an algorithm to solve it, and support our theoretical findings via experiments conducted on actual user data.

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Cited By

McAuley JPandey RLeskovec JCao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)Inferring Networks of Substitutable and Complementary ProductsProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2783381(785-794)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2783258.2783381
Panigrahi DGollapudi SLeonardi SPanconesi AFerragina PGionis A(2013)Document selection for tiered indexing in commerce searchProceedings of the sixth ACM international conference on Web search and data mining10.1145/2433396.2433408(73-82)Online publication date: 4-Feb-2013
https://dl.acm.org/doi/10.1145/2433396.2433408
Gollapudi SIeong SKannan AChen XLebanon GWang HZaki M(2012)Structured query reformulations in commerce searchProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398538(1890-1894)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398538
Show More Cited By

Index Terms

Result enrichment in commerce search using browse trails
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

February 2011

870 pages

ISBN:9781450304931

DOI:10.1145/1935826

General Chair:
Irwin King
CUHK, Hong Kong
,
Program Chairs:
Wolfgang Nejdl
L3S and University of Hannover, Germany
,
Hang Li
Microsoft Research Asia, China

Copyright © 2011 ACM.

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]

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Publication History

Published: 09 February 2011

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WSDM'11

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WSDM'11: Fourth ACM International Conference on Web Search and Data Mining

February 9 - 12, 2011

Hong Kong, China

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WSDM '11 Paper Acceptance Rate 83 of 372 submissions, 22%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

McAuley JPandey RLeskovec JCao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)Inferring Networks of Substitutable and Complementary ProductsProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2783381(785-794)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2783258.2783381
Panigrahi DGollapudi SLeonardi SPanconesi AFerragina PGionis A(2013)Document selection for tiered indexing in commerce searchProceedings of the sixth ACM international conference on Web search and data mining10.1145/2433396.2433408(73-82)Online publication date: 4-Feb-2013
https://dl.acm.org/doi/10.1145/2433396.2433408
Gollapudi SIeong SKannan AChen XLebanon GWang HZaki M(2012)Structured query reformulations in commerce searchProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398538(1890-1894)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398538
Dang VKumaran GTroy AChen XLebanon GWang HZaki M(2012)Domain dependent query reformulation for web searchProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398401(1045-1054)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398401
Gollapudi SIeong SNtoulas APaparizos S(2011)Efficient query rewrite for structured web queriesProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063981(2417-2420)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063981
Bhattacharya SGollapudi SMunagala KSadagopan SRamamritham KKumar ARavindra MBertino EKumar R(2011)Consideration set generation in commerce searchProceedings of the 20th international conference on World wide web10.1145/1963405.1963452(317-326)Online publication date: 28-Mar-2011
https://dl.acm.org/doi/10.1145/1963405.1963452

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