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Mining E-Commerce Query Relations using Customer Interaction Networks

Authors:

Bijaya Adhikari,

Parikshit Sondhi,

B. Aditya PrakashAuthors Info & Claims

WWW '18: Proceedings of the 2018 World Wide Web Conference

Pages 1805 - 1814

https://doi.org/10.1145/3178876.3186174

Published: 10 April 2018 Publication History

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Abstract

Customer Interaction Networks (CINs) are a natural framework for representing and mining customer interactions with E-Commerce search engines. Customer interactions begin with the submission of a query formulated based on an initial product intent, followed by a sequence of product engagement and query reformulation actions. Engagement with a product (e.g. clicks) indicates its relevance to the customer»s product intent. Reformulation to a new query indicates either dissatisfaction with current results, or an evolution in the customer»s product intent. Analyzing such interactions within and across sessions, enables us to discover various query-query and query-product relationships. In this work, we begin by studying the properties of CINs developed using Walmart.com»s product search logs. We observe that the properties exhibited by CINs make it possible to mine intent relationships between queries based purely on their structural information. We show how these relations can be exploited for a) clustering queries based on intents, b) significantly improve search quality for poorly performing queries, and c) identify the most influential (aka. »critical») queries whose performance have the highest impact on performance of other queries.

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Cited By

Han YHuang EZheng WRao NWang ZSubbian KChua TLauw HSi LTerzi ETsaparas P(2023)Search Behavior Prediction: A Hypergraph PerspectiveProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570403(697-705)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570403
Tuteja SKumar R(2022)Query-driven graph models in e-commerceInnovations in Systems and Software Engineering10.1007/s11334-021-00421-719:2(177-195)Online publication date: 14-Jan-2022
https://doi.org/10.1007/s11334-021-00421-7
Tsagkias MKing TKallumadi SMurdock Vde Rijke M(2021)Challenges and research opportunities in eCommerce search and recommendationsACM SIGIR Forum10.1145/3451964.345196654:1(1-23)Online publication date: 19-Feb-2021
https://dl.acm.org/doi/10.1145/3451964.3451966

Index Terms

Mining E-Commerce Query Relations using Customer Interaction Networks

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cover image ACM Other conferences

WWW '18: Proceedings of the 2018 World Wide Web Conference

April 2018

2000 pages

ISBN:9781450356398

General Chairs:
Pierre-Antoine Champin
Universitè Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

Copyright © 2018 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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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 10 April 2018

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WWW '18

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WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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WWW '18 Paper Acceptance Rate 170 of 1,155 submissions, 15%;

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

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Cited By

Han YHuang EZheng WRao NWang ZSubbian KChua TLauw HSi LTerzi ETsaparas P(2023)Search Behavior Prediction: A Hypergraph PerspectiveProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570403(697-705)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570403
Tuteja SKumar R(2022)Query-driven graph models in e-commerceInnovations in Systems and Software Engineering10.1007/s11334-021-00421-719:2(177-195)Online publication date: 14-Jan-2022
https://doi.org/10.1007/s11334-021-00421-7
Tsagkias MKing TKallumadi SMurdock Vde Rijke M(2021)Challenges and research opportunities in eCommerce search and recommendationsACM SIGIR Forum10.1145/3451964.345196654:1(1-23)Online publication date: 19-Feb-2021
https://dl.acm.org/doi/10.1145/3451964.3451966

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