skip to main content
10.1145/3292500.3330981acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Investigating Cognitive Effects in Session-level Search User Satisfaction

Published: 25 July 2019 Publication History

Abstract

User satisfaction is an important variable in Web search evaluation studies and has received more and more attention in recent years. Many studies regard user satisfaction as the ground truth for designing better evaluation metrics. However, most of the existing studies focus on designing Cranfield-like evaluation metrics to reflect user satisfaction at query-level. As information need becomes more and more complex, users often need multiple queries and multi-round search interactions to complete a search task (e.g. exploratory search). In those cases, how to characterize the user's satisfaction during a search session still remains to be investigated. In this paper, we collect a dataset through a laboratory study in which users need to complete some complex search tasks. With the help of hierarchical linear models (HLM), we try to reveal how user's query-level and session-level satisfaction are affected by different cognitive effects. A number of interesting findings are made. At query level, we found that although the relevance of top-ranked documents have important impacts (primacy effect), the average/maximum of perceived usefulness of clicked documents is a much better sign of user satisfaction. At session level, perceived satisfaction for a particular query is also affected by the other queries in the same session (anchor effect or expectation effect). We also found that session-level satisfaction correlates mostly with the last query in the session (recency effect). The findings will help us design better session-level user behavior models and corresponding evaluation metrics.

References

[1]
Azzah Al-Maskari, Mark Sanderson, and Paul D. Clough. 2007. The relationship between IR effectiveness measures and user satisfaction. In SIGIR'07.
[2]
Rashid Ali and MM Sufyan Beg. 2011. An overview of Web search evaluation methods. Computers & Electrical Engineering, Vol. 37, 6 (2011), 835--848.
[3]
AD Baddeley. 1968. Prior recall of newly learned items and the recency effect in free recall. Canadian Journal of Psychology/Revue canadienne de psychologie, Vol. 22, 3 (1968), 157.
[4]
Ernest R Cadotte, Robert B Woodruff, and Roger L Jenkins. 1987. Expectations and norms in models of consumer satisfaction. Journal of marketing Research (1987), 305--314.
[5]
Olivier Chapelle, Donald Metlzer, Ya Zhang, and Pierre Grinspan. 2009. Expected reciprocal rank for graded relevance. In CIKM'09.
[6]
Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008. An experimental comparison of click position-bias models. In WSDM'08 .
[7]
Henry A Feild, James Allan, and Rosie Jones. 2010. Predicting searcher frustration. In SIGIR'10 .
[8]
Steve Fox, Kuldeep Karnawat, Mark Mydland, Susan Dumais, and Thomas White. 2005. Evaluating implicit measures to improve web search. ACM Transactions on Information Systems (TOIS), Vol. 23, 2 (2005), 147--168.
[9]
Michael Gordon and Praveen Pathak. 1999. Finding information on the World Wide Web: the retrieval effectiveness of search engines. Information Processing & Management, Vol. 35, 2 (1999), 141--180.
[10]
Ahmed Hassan, Ryen W White, Susan T Dumais, and Yi-Min Wang. 2014. Struggling or exploring?: disambiguating long search sessions. In WSDM'14 .
[11]
Scott B Huffman and Michael Hochster. 2007. How well does result relevance predict session satisfaction?. In SIGIR'07 .
[12]
Jaana Kekalainen Kalervo Jarvelin. 2003. User-oriented evaluation methods for information retrieval: A case study based on conceptual models for query expansion. Exploring artificial intelligence in the new millennium (2003), 355.
[13]
Kalervo J"arvelin and Jaana Kek"al"ainen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), Vol. 20, 4 (2002), 422--446.
[14]
Kalervo J"arvelin, Susan L Price, Lois ML Delcambre, and Marianne Lykke Nielsen. 2008. Discounted cumulated gain based evaluation of multiple-query IR sessions. In ECIR'08 .
[15]
Jiepu Jiang, Ahmed Hassan Awadallah, Xiaolin Shi, and Ryen W. White. 2015. Understanding and Predicting Graded Search Satisfaction. In WSDM'15 .
[16]
Jiepu Jiang, Daqing He, and James Allan. 2017a. Comparing In Situ and Multidimensional Relevance Judgments. In SIGIR'17 .
[17]
Jiepu Jiang, Daqing He, Diane Kelly, and James Allan. 2017b. Understanding ephemeral state of relevance. In CHIIR'17 .
[18]
Diane Kelly. 2009. Methods for Evaluating Interactive Information Retrieval Systems with Users. Foundations and Trends in Information Retrieval, Vol. 3, 1--2 (2009), 1--224.
[19]
Youngho Kim, Ahmed Hassan Awadallah, Ryen W. White, and Imed Zitouni. 2014. Modeling dwell time to predict click-level satisfaction. In WSDM'14 .
[20]
Matthew Lease and Emine Yilmaz. 2012. Crowdsourcing for information retrieval. In SIGIR'12 .
[21]
Mengyang Liu, Yiqun Liu, Jiaxin Mao, Cheng Luo, and Shaoping Ma. 2018a. Towards Designing Better Session Search Evaluation Metrics. In SIGIR'18 .
[22]
Mengyang Liu, Yiqun Liu, Jiaxin Mao, Cheng Luo, Min Zhang, and Shaoping Ma. 2018b. "Satisfaction with Failure" or "Unsatisfied Success": Investigating the Relationship between Search Success and User Satisfaction. In WWW'18 .
[23]
Yiqun Liu, Ye Chen, Jinhui Tang, Jiashen Sun, Min Zhang, Shaoping Ma, and Xuan Zhu. 2015. Different Users, Different Opinions: Predicting Search Satisfaction with Mouse Movement Information. In SIGIR'15 .
[24]
Jiyun Luo, Christopher Wing, Hui Yang, and Marti A. Hearst. 2013. The water filling model and the cube test: multi-dimensional evaluation for professional search. In CIKM'13 .
[25]
Jiaxin Mao, Yiqun Liu, Ke Zhou, Jian-Yun Nie, Jingtao Song, Min Zhang, Shaoping Ma, Jiashen Sun, and Hengliang Luo. 2016. When does Relevance Mean Usefulness and User Satisfaction in Web Search?. In SIGIR'16 .
[26]
Alistair Moffat, Paul Thomas, and Falk Scholer. 2013. Users versus models: What observation tells us about effectiveness metrics. In CIKM'13 .
[27]
Alistair Moffat and Justin Zobel. 2008. Rank-biased precision for measurement of retrieval effectiveness. ACM Transactions on Information Systems (TOIS), Vol. 27, 1 (2008), 2.
[28]
Mark D Smucker and Charles LA Clarke. 2012. Time-based calibration of effectiveness measures. In SIGIR'12 .
[29]
Amos Tversky and Daniel Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. science, Vol. 185, 4157 (1974), 1124--1131.
[30]
Ellen M Voorhees. 2000. Variations in relevance judgments and the measurement of retrieval effectiveness. Information processing & management, Vol. 36, 5 (2000), 697--716.
[31]
Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ahmed Hassan, and Ryen W White. 2014. Modeling action-level satisfaction for search task satisfaction prediction. In SIGIR'14 .
[32]
Nancy C Waugh and Donald A Norman. 1965. Primary memory. Psychological review, Vol. 72, 2 (1965), 89.
[33]
Ya Xu and David Mease. 2009. Evaluating web search using task completion time. In SIGIR'09 .
[34]
Yiming Yang and Abhimanyu Lad. 2009. Modeling expected utility of multi-session information distillation. In ITCIR'09 .

Cited By

View all
  • (2025)Session-Level Normalization and Click-Through Data Enhancement for Session-Based EvaluationBig Data10.1007/978-981-96-1024-2_2(15-33)Online publication date: 24-Jan-2025
  • (2024)Decoy Effect in Search Interaction: Understanding User Behavior and Measuring System VulnerabilityACM Transactions on Information Systems10.1145/370888443:2(1-58)Online publication date: 19-Dec-2024
  • (2024)AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance AssessmentProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698420(54-63)Online publication date: 8-Dec-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
July 2019
3305 pages
ISBN:9781450362016
DOI:10.1145/3292500
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. search evaluation
  2. session search
  3. user satisfaction

Qualifiers

  • Research-article

Conference

KDD '19
Sponsor:

Acceptance Rates

KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)62
  • Downloads (Last 6 weeks)7
Reflects downloads up to 27 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Session-Level Normalization and Click-Through Data Enhancement for Session-Based EvaluationBig Data10.1007/978-981-96-1024-2_2(15-33)Online publication date: 24-Jan-2025
  • (2024)Decoy Effect in Search Interaction: Understanding User Behavior and Measuring System VulnerabilityACM Transactions on Information Systems10.1145/370888443:2(1-58)Online publication date: 19-Dec-2024
  • (2024)AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance AssessmentProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698420(54-63)Online publication date: 8-Dec-2024
  • (2024)Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated TopicsProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672520(227-237)Online publication date: 2-Aug-2024
  • (2024)USimAgent: Large Language Models for Simulating Search UsersProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657963(2687-2692)Online publication date: 10-Jul-2024
  • (2023)Validating Synthetic Usage Data in Living Lab EnvironmentsJournal of Data and Information Quality10.1145/3623640Online publication date: 24-Sep-2023
  • (2023)Measuring In-Task Emotional Responses to Address Issues in Post-Task QuestionnairesProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578284(482-485)Online publication date: 19-Mar-2023
  • (2023)A Reference-Dependent Model for Web Search EvaluationProceedings of the ACM Web Conference 202310.1145/3543507.3583551(3396-3405)Online publication date: 30-Apr-2023
  • (2023)Constructing and meta-evaluating state-aware evaluation metrics for interactive search systemsInformation Retrieval Journal10.1007/s10791-023-09426-126:1-2Online publication date: 31-Oct-2023
  • (2023)Implications and New Directions for IR Research and PracticesA Behavioral Economics Approach to Interactive Information Retrieval10.1007/978-3-031-23229-9_7(181-201)Online publication date: 18-Feb-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media