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Investigation of User Search Behavior While Facing Heterogeneous Search Services

Published: 02 February 2017 Publication History

Abstract

With Web users' search tasks becoming increasingly complex, a single information source cannot necessarily satisfy their information needs. Searchers may rely on heterogeneous sources to complete their tasks, such as search engines, Community Question Answering (CQA), encyclopedia sites, and crowdsourcing platforms. Previous works focus on interaction behaviors with federated search results, including how to compose a federated Web search result page and what factors affect users' interaction behavior on aggregated search interfaces. However, little is known about which factors are crucial in determining users' search outcomes while facing multiple heterogeneous search services. In this paper, we design a lab-based user study to analyze what explicit and implicit factors affect search outcomes (information gain and user satisfaction) when users have access to heterogeneous information sources. In the study, each participant can access three different kinds of search services: a general search engine (Bing), a general CQA portal (Baidu Knows), and a high-quality CQA portal (Zhihu). Using questionnaires and interaction log data, we extract explicit and implicit signals to analyze how users' search outcomes are correlated with their behaviors on different information sources. Experimental results indicate that users' search experiences on CQA portals (such as users' perceived usefulness and number of result clicks) positively affect search outcome (information gain), while search satisfaction is significantly correlated with some other factors such as users' familiarity, interest and difficulty of the task. Besides, users' search satisfaction can be more accurately predicted by the implicit factors than search outcomes.

References

[1]
A. Afuah and C. L. Tucci. Crowdsourcing as a solution to distant search. Academy of Management Review, 37(3):355--375, 2012.
[2]
J. Arguello, F. Diaz, J. Callan, and B. Carterette. A methodology for evaluating aggregated search results. In European Conference on Information Retrieval, pages 141--152. Springer, 2011.
[3]
J. Arguello, F. Diaz, J. Callan, and J.-F. Crespo. Sources of evidence for vertical selection. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 315--322. ACM, 2009.
[4]
J. Arguello, W.-C. Wu, D. Kelly, and A. Edwards. Task complexity, vertical display and user interaction in aggregated search. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pages 435--444. ACM, 2012.
[5]
O. Boydell and B. Smyth. Capturing community search expertise for personalized web search using snippet-indexes. In Proceedings of the 15th ACM international conference on Information and knowledge management, pages 277--286. ACM, 2006.
[6]
A. Chuklin, K. Zhou, A. Schuth, F. Sietsma, and M. De Rijke. Evaluating intuitiveness of vertical-aware click models. In Proceedings of the 37th international ACM SIGIR conference on Research and development in information retrieval, pages 1075--1078. ACM, 2014.
[7]
K. Collins-Thompson, S. Y. Rieh, C. C. Haynes, and R. Syed. Assessing learning outcomes in web search: A comparison of tasks and query strategies. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval, pages 163--172. ACM, 2016.
[8]
Q. Guo, D. Lagun, and E. Agichtein. Predicting web search success with fine-grained interaction data. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 2050--2054. ACM, 2012.
[9]
R. Hahn, C. Bizer, C. Sahnwaldt, C. Herta, S. Robinson, M. Bürgle, H. Düwiger, and U. Scheel. Faceted wikipedia search. In Business Information Systems, pages 1--11. Springer, 2010.
[10]
A. Hassan, X. Shi, N. Craswell, and B. Ramsey. Beyond clicks: query reformulation as a predictor of search satisfaction. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, pages 2019--2028. ACM, 2013.
[11]
J. Jiang, A. Hassan Awadallah, X. Shi, and R. W. White. Understanding and predicting graded search satisfaction. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pages 57--66. ACM, 2015.
[12]
Y. Liu, J. Bian, and E. Agichtein. Predicting information seeker satisfaction in community question answering. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 483--490. ACM, 2008.
[13]
Y. Liu, Y. Chen, J. Tang, J. Sun, M. Zhang, S. Ma, and X. Zhu. Different users, different opinions: Predicting search satisfaction with mouse movement information. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 493--502. ACM, 2015.
[14]
Y. Liu, Z. Liu, K. Zhou, M. Wang, H. Luan, C. Wang, M. Zhang, and S. Ma. Predicting search user examination with visual saliency. pages 619--628, 2016.
[15]
D. C. Montgomery. Design and analysis of experiments. John Wiley & Sons, 2008.
[16]
N. Moraveji. User interface designs to support the social transfer of web search expertise. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 915--915. ACM, 2010.
[17]
N. Moraveji, S. Ahmad, C. Kita, F. Chen, and S. Kamvar. Weblines: Enabling the social transfer of web search expertise using user-generated short-form timelines. In Proceedings of the 9th International Computer-Supported Collaborative Learning Conference, pages 112--119. ISLS, 2011.
[18]
A. K. Ponnuswami, K. Pattabiraman, Q. Wu, R. Gilad-Bachrach, and T. Kanungo. On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 715--724. ACM, 2011.
[19]
Y. Sun and Y. Zhang. Individual differences and online health information source selection. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval, pages 321--324. ACM, 2016.
[20]
S. Sushmita, H. Joho, M. Lalmas, and R. Villa. Factors affecting click-through behavior in aggregated search interfaces. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 519--528. ACM, 2010.
[21]
H. Wang, Y. Song, M.-W. Chang, X. He, A. Hassan, and R. W. White. Modeling action-level satisfaction for search task satisfaction prediction. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 123--132. ACM, 2014.
[22]
R. W. White and D. Morris. Investigating the querying and browsing behavior of advanced search engine users. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 255--262. ACM, 2007.
[23]
K. Zhou, R. Cummins, M. Lalmas, and J. M. Jose. Evaluating aggregated search pages. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pages 115--124. ACM, 2012.
[24]
K. Zhou, R. Cummins, M. Lalmas, and J. M. Jose. Which vertical search engines are relevant? In Proceedings of the 22nd international conference on World Wide Web, pages 1557--1568. ACM, 2013.
[25]
K. Zhou, T. Sakai, M. Lalmas, Z. Dou, and J. M. Jose. Evaluating heterogeneous information access. In Proceedings of MUBE workshop, 2013.

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    cover image ACM Conferences
    WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
    February 2017
    868 pages
    ISBN:9781450346757
    DOI:10.1145/3018661
    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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    Published: 02 February 2017

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    Author Tags

    1. heterogeneous information
    2. search outcome
    3. user study

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    WSDM '17 Paper Acceptance Rate 80 of 505 submissions, 16%;
    Overall Acceptance Rate 498 of 2,863 submissions, 17%

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    • (2021)An architecture for non-linear discovery of aggregated multimedia document web search resultsPeerJ Computer Science10.7717/peerj-cs.4497(e449)Online publication date: 21-Apr-2021
    • (2021)A Relational Aggregated Disjoint Multimedia Search Results Approach using Semantics2021 International Conference on Artificial Intelligence (ICAI)10.1109/ICAI52203.2021.9445229(62-67)Online publication date: 5-Apr-2021
    • (2021)Web Augmentation as a Technique to Diminish User Interactions in Repetitive TasksIEEE Access10.1109/ACCESS.2021.31041879(112686-112704)Online publication date: 2021
    • (2021)An Analysis on User Behaviors in Online Question and Answering CommunitiesComputer Supported Cooperative Work and Social Computing10.1007/978-981-16-2540-4_34(469-483)Online publication date: 7-May-2021
    • (2019)Customizing Websites Through Automatic Web SearchHuman-Computer Interaction – INTERACT 201910.1007/978-3-030-29384-0_36(598-618)Online publication date: 25-Aug-2019
    • (2018)Mobile Search Behavious: An In-depth Analysis based on Contexts, APPs, and DevicesSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00831ED1V01Y201802ICR06310:2(i-159)Online publication date: 19-Mar-2018
    • (2018)How Does Domain Expertise Affect Users’ Search Interaction and Outcome in Exploratory Search?ACM Transactions on Information Systems10.1145/322304536:4(1-30)Online publication date: 17-Jul-2018
    • (2018)"Satisfaction with Failure" or "Unsatisfied Success"Proceedings of the 2018 World Wide Web Conference10.1145/3178876.3186065(1533-1542)Online publication date: 10-Apr-2018

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