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Investigating the Reliability of Click Models

Authors:

Shaoping MaAuthors Info & Claims

ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval

Pages 125 - 128

https://doi.org/10.1145/3341981.3344242

Published: 26 September 2019 Publication History

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Abstract

Click models aim to extract accurate relevance feedback from the noisy and biased user clicks. Previous work focuses on reducing the systematic bias between click and relevance but few studies have examined the reliability and precision of click models' relevance estimation. So in this study, we propose to investigate the reliability of relevance estimation derived by click models. Instead of getting a point estimate of relevance, a variational Bayesian method is used to infer the posterior distribution of relevance parameters. Based on the posterior distribution, we define measures for the reliability of pointwise and pairwise relevance estimation. With experiments on both real and synthetic query logs, we show that: 1) the proposed method effectively captures the uncertainty in relevance estimation; 2) the reliability of click models' relevance estimation is affected by the size of training data, the average ranking position of documents, and the ranking strategy of search engines.

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Cited By

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Luo DZou LAi QChen ZLi CYin DDavison BHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Unbiased Learning-to-Rank Needs Unconfounded Propensity EstimationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657772(1535-1545)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657772
Breuer TFuhr NSchaer P(2023)Validating Synthetic Usage Data in Living Lab EnvironmentsJournal of Data and Information Quality10.1145/3623640Online publication date: 24-Sep-2023
https://dl.acm.org/doi/10.1145/3623640
Zou LHao CCai HWang SCheng SCheng ZYe WGu SYin DAl Hasan MXiong L(2022)Approximated Doubly Robust Search Relevance EstimationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557145(3756-3765)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557145
Show More Cited By

Index Terms

Investigating the Reliability of Click Models
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
  2. World Wide Web
    1. Web searching and information discovery
      1. Web search engines
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Bayesian analysis

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Information & Contributors

Information

Published In

ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval

September 2019

273 pages

ISBN:9781450368810

DOI:10.1145/3341981

General Chairs:
Yi Fang
Santa Clara University, USA
,
Yi Zhang
University of California, Santa Cruz, USA
,
Program Chairs:
James Allan
University of Massachusetts, Amherst, USA
,
Krisztian Balog
University of Stavanger, Norway
,
Ben Carterette
Spotify, USA
,
Jiafeng Guo
Chinese Academy of Sciences, China

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2019

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Short-paper

Funding Sources

National Key Research and Development Program of China
Natural Science Foundation of China

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ICTIR '19

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SIGIR

ICTIR '19: The 2019 ACM SIGIR International Conference on the Theory of Information Retrieval

October 2 - 5, 2019

CA, Santa Clara, USA

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ICTIR '19 Paper Acceptance Rate 20 of 41 submissions, 49%;

Overall Acceptance Rate 235 of 527 submissions, 45%

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Cited By

View all

Luo DZou LAi QChen ZLi CYin DDavison BHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Unbiased Learning-to-Rank Needs Unconfounded Propensity EstimationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657772(1535-1545)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657772
Breuer TFuhr NSchaer P(2023)Validating Synthetic Usage Data in Living Lab EnvironmentsJournal of Data and Information Quality10.1145/3623640Online publication date: 24-Sep-2023
https://dl.acm.org/doi/10.1145/3623640
Zou LHao CCai HWang SCheng SCheng ZYe WGu SYin DAl Hasan MXiong L(2022)Approximated Doubly Robust Search Relevance EstimationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557145(3756-3765)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557145
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
Dai XXi YZhang WLiu QTang RHe XHou JWang JYu Y(2021)Beyond Relevance Ranking: A General Graph Matching Framework for Utility-Oriented Learning to RankACM Transactions on Information Systems10.1145/346430340:2(1-29)Online publication date: 16-Nov-2021
https://dl.acm.org/doi/10.1145/3464303
Ai QYang TWang HMao J(2021)Unbiased Learning to RankACM Transactions on Information Systems10.1145/343986139:2(1-29)Online publication date: 17-Feb-2021
https://dl.acm.org/doi/10.1145/3439861

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Constructing Click Models for Mobile Search

A Context-Aware Click Model for Web Search

Characterizing search intent diversity into click models