research-article

Content-aware click modeling

Authors:

ChengXiang Zhai,

Yi ChangAuthors Info & Claims

WWW '13: Proceedings of the 22nd international conference on World Wide Web

Pages 1365 - 1376

https://doi.org/10.1145/2488388.2488508

Published: 13 May 2013 Publication History

Abstract

Click models aim at extracting intrinsic relevance of documents to queries from biased user clicks. One basic modeling assumption made in existing work is to treat such intrinsic relevance as an atomic query-document-specific parameter, which is solely estimated from historical clicks without using any content information about a document or relationship among the clicked/skipped documents under the same query. Due to this overly simplified assumption, existing click models can neither fully explore the information about a document's relevance quality nor make predictions of relevance for any unseen documents.

In this work, we proposed a novel Bayesian Sequential State model for modeling the user click behaviors, where the document content and dependencies among the sequential click events within a query are characterized by a set of descriptive features via a probabilistic graphical model. By applying the posterior regularized Expectation Maximization algorithm for parameter learning, we tailor the model to meet specific ranking-oriented properties, e.g., pairwise click preferences, so as to exploit richer information buried in the user clicks. Experiment results on a large set of real click logs demonstrate the effectiveness of the proposed model compared with several state-of-the-art click models.

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Cited By

Jianping LYingfei WJian WMeng WXintao C(2024)A topic relevance-aware click model for web searchJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23689446:4(8961-8974)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-236894
Buchholz ALondon BDi Benedetto GLichtenberg JStein YJoachims THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Counterfactual Ranking Evaluation with Flexible Click ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657810(1200-1210)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657810
Liu JWang YWang JWang MChu X(2024)Probabilistic graph model and neural network perspective of click models for web searchKnowledge and Information Systems10.1007/s10115-024-02145-z66:10(5829-5873)Online publication date: 6-Jun-2024
https://doi.org/10.1007/s10115-024-02145-z
Show More Cited By

Index Terms

Content-aware click modeling
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
  2. World Wide Web

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Published In

cover image ACM Other conferences

WWW '13: Proceedings of the 22nd international conference on World Wide Web

May 2013

1628 pages

ISBN:9781450320351

DOI:10.1145/2488388

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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CGIBR

WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;

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

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Cited By

Jianping LYingfei WJian WMeng WXintao C(2024)A topic relevance-aware click model for web searchJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23689446:4(8961-8974)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-236894
Buchholz ALondon BDi Benedetto GLichtenberg JStein YJoachims THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Counterfactual Ranking Evaluation with Flexible Click ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657810(1200-1210)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657810
Liu JWang YWang JWang MChu X(2024)Probabilistic graph model and neural network perspective of click models for web searchKnowledge and Information Systems10.1007/s10115-024-02145-z66:10(5829-5873)Online publication date: 6-Jun-2024
https://doi.org/10.1007/s10115-024-02145-z
Luo DZou LAi QChen ZYin DDavison BChua TLauw HSi LTerzi ETsaparas P(2023)Model-based Unbiased Learning to RankProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570395(895-903)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570395
Fu LLin JLiu WTang RZhang WZhang RYu YChua TLauw HSi LTerzi ETsaparas P(2023)An F-shape Click Model for Information Retrieval on Multi-block Mobile PagesProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570365(1057-1065)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570365
Fairstein YHaramaty ELazerson ALewin-Eytan LSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)External Evaluation of Ranking Models under Extreme Position-BiasProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498420(252-261)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498420
Khandel PMarkov IYates AVarbanescu A(2022)ParClick: A Scalable Algorithm for EM-based Click ModelsProceedings of the ACM Web Conference 202210.1145/3485447.3511967(392-400)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3511967
Wang NQin ZWang XWang HLewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a CorrectionProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441798(481-489)Online publication date: 8-Mar-2021
https://dl.acm.org/doi/10.1145/3437963.3441798
Lin JLiu WDai XZhang WLi STang RHe XHao JYu YDiaz FShah CSuel TCastells PJones RSakai T(2021)A Graph-Enhanced Click Model for Web SearchProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462895(1259-1268)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462895
Yu HJatowt ABlanco RJose JZhou KAzzopardi LHalvey MRuthven IJoho HMurdock VQvarfordt P(2019)A Rank-biased Neural Network Model for Click ModelingProceedings of the 2019 Conference on Human Information Interaction and Retrieval10.1145/3295750.3298920(183-191)Online publication date: 8-Mar-2019
https://dl.acm.org/doi/10.1145/3295750.3298920
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