research-article

Playing Your Cards Right: The Effect of Entity Cards on Search Behaviour and Workload

Authors:

Joemon M. JoseAuthors Info & Claims

CHIIR '16: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval

Pages 131 - 140

https://doi.org/10.1145/2854946.2854967

Published: 13 March 2016 Publication History

Abstract

In addition to merging results of different types (e.g.~images, videos, news items) into a ranked list of Web documents, modern search engines have also started displaying entity cards (ECs) on the results page. Entity cards are intended to enhance search experience in several ways: (i) they help searchers navigate diversified results, (ii) provide a summary of relevant content directly on the results page and (iii) support exploratory search by highlighting relevant entities associated with a given user query. We conducted a large-scale crowd-sourced user study, with more than $700$ unique searchers, to investigate the effects of entity cards on search behaviour and perceived workload. We find that the presence of ECs has a strong effect on both the way users interact with search results and their perceived task workload. Furthermore, by manipulating EC properties content, coherence and vertical diversity), we uncover different effects and interactions between card properties on measures of search behaviour and workload. Our study contributes an in-depth analysis of the effects of entity cards on user interaction with modern Web search interfaces.

References

[1]

J. Arguello and R. Capra. The effect of aggregated search coherence on search behavior. ACM CIKM '12, pages 1293--1302.

Digital Library

[2]

J. Arguello and R. Capra. The effects of vertical rank and border on aggregated search coherence and search behavior. ACM CIKM '14, pages 539--548, 2014.

Digital Library

[3]

J. Arguello, R. Capra, and W.-C. Wu. Factors affecting aggregated search coherence and search behavior. ACM CIKM '13, pages 1989--1998.

Digital Library

[4]

J. Arguello, W.-C. Wu, D. Kelly, and A. Edwards. Task complexity, vertical display and user interaction in aggregated search. ACM SIGIR '12, pages 435--444.

Digital Library

[5]

A. Ashkan, C. Clarke, E. Agichtein, and Q. Guo. Classifying and characterizing query intent. In M. Boughanem, C. Berrut, J. Mothe, and C. Soule-Dupuy, editors, Advances in Information Retrieval, volume 5478 of Lecture Notes in Computer Science, pages 578--586. Springer Berlin Heidelberg, 2009.

Digital Library

[6]

R. Blanco, B. B. Cambazoglu, P. Mika, and N. Torzec. Entity recommendations in web search. In The Semantic Web--ISWC 2013, pages 33--48. Springer, 2013.

Digital Library

[7]

D. J. Brenes, D. Gayo-Avello, and K. Pérez-González. Survey and evaluation of query intent detection methods. In Proceedings of the 2009 Workshop on Web Search Click Data, WSCD '09, pages 1--7, New York, NY, USA, 2009. ACM.

Digital Library

[8]

K. Brennan, D. Kelly, and J. Arguello. The effect of cognitive abilities on information search for tasks of varying levels of complexity. ACM IIiX '14, pages 165--174.

Digital Library

[9]

R. Capra, J. Arguello, and F. Scholer. Augmenting web search surrogates with images. ACM CIKM '13, pages 399--408, 2013.

Digital Library

[10]

F. Diaz, R. White, G. Buscher, and D. Liebling. Robust models of mouse movement on dynamic web search results pages. ACM CIKM '13, pages 1451--1460.

Digital Library

[11]

S. G. Hart and L. E. Staveland. Development of nasa-tlx (task load index): Results of empirical and theoretical research. In P. A. Hancock and N. Meshkati, editors, Human Mental Workload, volume 52 of Advances in Psychology, pages 139--183. North-Holland, 1988.

[12]

S. Holm. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2):pp. 65--70, 1979.

[13]

C. J. Huberty and J. D. Morris. Multivariate analysis versus multiple univariate analyses. Psychological Bulletin, 105(2):302--308, 1989.

[14]

D. Lagun, C.-H. Hsieh, D. Webster, and V. Navalpakkam. Towards better measurement of attention and satisfaction in mobile search. In ACM SIGIR '14, pages 113--122.

Digital Library

[15]

Z. Liu, Y. Liu, K. Zhou, M. Zhang, and S. Ma. Influence of vertical result in web search examination. SIGIR '15, pages 193--202, New York, NY, USA, 2015. ACM.

Digital Library

[16]

T. Megaw. The definition and measurement of mental workload. In J. R. and E. N. Corlett, editors, Evaluation of Human Work. CRC Press, 2005.

[17]

M. Melucci and R. Baeza-Yates. Advanced topics in information retrieval. pages 109--123. Springer Publishing Company, 2011.

Digital Library

[18]

J. Meyers-Levy and B. Sternthal. A two-factor explanation of assimilation and contrast effects. Journal of Marketing Research, 30(3):pp. 359--368, 1993.

[19]

A. C. Morales and G. J. Fitzsimons. Product contagion: Changing consumer evaluations through physical contact with "disgusting" products. Journal of Marketing Research, 44(2):272--283, 2007.

[20]

V. Navalpakkam, L. Jentzsch, R. Sayres, S. Ravi, A. Ahmed, and A. Smola. Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts. In ACM WWW '13, pages 953--964.

Digital Library

[21]

M. Sanderson. Ambiguous queries: Test collections need more sense. ACM SIGIR '08, pages 499--506.

Digital Library

[22]

C. Speier and M. G. Morris. The influence of query interface design on decision-making performance. MIS Q., 27(3):397--423, Sept. 2003.

[23]

L. L. D. Stasi, A. Antoli, M. Gea, and J. J. Canas. A neuroergonomic approach to evaluating mental workload in hypermedia interactions. International Journal of Industrial Ergonomics, 41(3):298 -- 304, 2011.

[24]

S. Sushmita, H. Joho, M. Lalmas, and R. Villa. Factors affecting click-through behavior in aggregated search interfaces. In ACM CIKM '10, pages 519--528. ACM, 2010.

Digital Library

[25]

C. Wang, Y. Liu, M. Zhang, S. Ma, M. Zheng, J. Qian, and K. Zhang. Incorporating vertical results into search click models. ACM SIGIR '13, pages 503--512.

Digital Library

Cited By

Marchesin SSilvello GAlonso OSerra ESpezzano F(2024)Veracity Estimation for Entity-Oriented Search with Knowledge GraphsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679561(1649-1659)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679561
Momeni M(2024)Visualization-Enhanced Aggregated Search InterfacesProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638336(461-464)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638336
Pathak KAzzopardi LHalvey M(2024)The Influence of Presentation and Performance on User SatisfactionProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638335(77-86)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638335
Show More Cited By

Index Terms

Playing Your Cards Right: The Effect of Entity Cards on Search Behaviour and Workload
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. Interaction paradigms
      1. Web-based interaction
2. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Search interfaces

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cover image ACM Conferences

CHIIR '16: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval

March 2016

400 pages

ISBN:9781450337519

DOI:10.1145/2854946

General Chairs:
Diane Kelly
University of North Carolina at Chapel Hill, USA
,
Rob Capra
University of North Carolina at Chapel Hill, USA
,
Program Chairs:
Nick Belkin
Rutgers University, USA
,
Jaime Teevan
Microsoft Research, USA
,
Pertti Vakkari
University of Tampere, Finland

Copyright © 2016 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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SIGIR: ACM Special Interest Group on Information Retrieval

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 13 March 2016

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CHIIR '16: Conference on Human Information Interaction and Retrieval

March 13 - 17, 2016

North Carolina, Carrboro, USA

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CHIIR '16 Paper Acceptance Rate 23 of 58 submissions, 40%;

Overall Acceptance Rate 55 of 163 submissions, 34%

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Cited By

Marchesin SSilvello GAlonso OSerra ESpezzano F(2024)Veracity Estimation for Entity-Oriented Search with Knowledge GraphsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679561(1649-1659)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679561
Momeni M(2024)Visualization-Enhanced Aggregated Search InterfacesProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638336(461-464)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638336
Pathak KAzzopardi LHalvey M(2024)The Influence of Presentation and Performance on User SatisfactionProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638335(77-86)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638335
Rieger ADraws TMattis NMaxwell DElsweiler DGadiraju UMcKay DBozzon APera M(2024)Responsible Opinion Formation on Debated Topics in Web SearchAdvances in Information Retrieval10.1007/978-3-031-56066-8_32(437-465)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56066-8_32
Pathak KAzzopardi LHalvey M(2024)Ranking Heterogeneous Search Result Pages Using the Interactive Probability Ranking PrincipleAdvances in Information Retrieval10.1007/978-3-031-56060-6_7(96-110)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_7
McGregor MAzzopardi LHalvey M(2023)A Systematic Review of Cost, Effort, and Load Research in Information Search and Retrieval, 1972–2020ACM Transactions on Information Systems10.1145/358306942:1(1-39)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1145/3583069
Azzopardi LMaxwell DHalvey MHauff C(2023)Driven to Distraction: Examining the Influence of Distractors on Search Behaviours, Performance and ExperienceProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578298(83-94)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578298
Wang PYu MLiu Y(2022)Assessing the Content Topics of the Educational Videos on Tik Tok for Science CommunicationProceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)10.2991/978-2-494069-31-2_210(1792-1801)Online publication date: 29-Dec-2022
https://doi.org/10.2991/978-2-494069-31-2_210
Ruotsalo TWeber SGajos K(2022)Active tag recommendation for interactive entity searchInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10285659:2Online publication date: 9-May-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102856
Shao YMao JLiu YZhang MMa S(2022)From linear to non-linear: investigating the effects of right-rail results on complex SERPsAdvances in Computational Intelligence10.1007/s43674-021-00028-22:1Online publication date: 10-Jan-2022
https://doi.org/10.1007/s43674-021-00028-2
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