research-article

Personalized web exploration with task models

Authors:

Peter Brusilovsky,

Jonathan Grady,

Qi LiAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 1 - 10

https://doi.org/10.1145/1367497.1367499

Published: 21 April 2008 Publication History

Abstract

Personalized Web search has emerged as one of the hottest topics for both the Web industry and academic researchers. However, the majority of studies on personalized search focused on a rather simple type of search, which leaves an important research topic - the personalization in exploratory searches - as an under-studied area. In this paper, we present a study of personalization in task-based information exploration using a system called TaskSieve. TaskSieve is a Web search system that utilizes a relevance feedback based profile, called a "task model", for personalization. Its innovations include flexible and user controlled integration of queries and task models, task-infused text snippet generation, and on-screen visualization of task models. Through an empirical study using human subjects conducting task-based exploration searches, we demonstrate that TaskSieve pushes significantly more relevant documents to the top of search result lists as compared to a traditional search system. TaskSieve helps users select significantly more accurate information for their tasks, allows the users to do so with higher productivity, and is viewed more favorably by subjects under several usability related characteristics.

References

[1]

Ahn, J.-w., Brusilovsky, P., Grady, J., He, D., Syn, S. Y. Open user profiles for adaptive news systems: help or harm? in Proceedings of the 16th international conference on World Wide Web, WWW '07 (Banff, Canada), ACM 11--20.

Digital Library

[2]

Allan, J. HARD Track Overview in TREC 2003 High Accuracy Retrieval from Documents in Proceedings of The Twelfth Text Retrieval Conference.

[3]

Chen, L., Sycara, K. WebMate: A personal agent for browsing and searching in Proceedings of 2nd International Conference on Autonomous Agents (Agents'98) (St. Paul, MN), ACM Press 132--139.

Digital Library

[4]

Claypool, M., Le, P., Wased, M., Brown, D. Implicit interest indicators in Proceedings of 6th International Conference on Intelligent User Interfaces (Santa Fe, NM, January 14--17, 2002), ACM Press 33--40.

Digital Library

[5]

Díaz, A., Gervás, P. Personalisation in news delivery systems: Item summarization and multi-tier item selection using relevance feedback, Web Intelligence and Agent Systems (2005) 3, 3, 135--154.

Digital Library

[6]

Dorr, B., Zajic, D., Schwartz, R. Cross Language Headline Generation for Hindi, This volume (2003).

Digital Library

[7]

Fox, S., Karnawat, K., Mydland, M., Dumais, S., White, T. Evaluating implicit measures to improve web search, ACM Transactions on Information Systems (2005) 23, 2, 147--168.

Digital Library

[8]

Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A. User profiles for personalized information access, In: Brusilovsky, P., Kobsa, A., and Neidl, W., Eds. The Adaptive Web: Methods and Strategies of Web Personalization. Springer-Verlag, Berlin Heidelberg New York, 2007, 54--89.

Digital Library

[9]

Harman, D. K. Relevance feedback revisited in Proceedings of Proceedings of ACM-SIGIR 92.

Digital Library

[10]

He, D., Brusilovsky, P., Grady, J., Li, Q., Ahn, J.-w. How Up-to-date should it be? The Value of Instant Profiling and Adaptation in Information Filtering in Proceedings of the 2007 international conference on Web Intelligence, WI '07 (Silicon Valey, CA, USA), IEEE in press.

Digital Library

[11]

He, D. et al. An Evaluation of Adaptive Filtering in the Context of Realistic Task-Based Information Exploration, Information Processing and Management (2007).

Digital Library

[12]

He, D. et al. Making MIRACLEs: Interactive Translingual Search for Cebuano and Hindi, ACM Transactions on Asian Lnaguage Information Processing (2003) 2, 3, 219--244.

Digital Library

[13]

He, D., Wang, J., Luo, J., Oard, D.W. iCLEF 2004 at Maryland: Summarization Design for Interactive Cross-Language Question Answering in Proceedings of The Proceeding of Cross-Language Evaluation Forum (CLEF 2004).

Digital Library

[14]

Houseman, E. M., Kaskela, D. E. State of the art of selective dissemination of information, IEEE Transactions on Engineering Writing and Speech (1970) 13, 2, 78--83.

[15]

Jung, S., Harris, K., Webster, J., Herlocker, J. L. SERF: Iintegrating human recommendations with search in Proceedings of ACM 13th Conference on Information and Knowledge Management, CIKM 2004 (Washington, DC, November 8--13, 2004 571--580.

Digital Library

[16]

Kantor, P.B., Boros, E., Melamed, B., Meñkov, V., Shapira, B., Neu, D. J. Capturing human intelligence in the net, Communications of the ACM (2000) 43, 8, 112--116.

Digital Library

[17]

Kelly, D., Teevan, J. Implicit feedback for inferring user preference: a bibliography, SIGIR Forum (2003) 37, 2, 18--28.

Digital Library

[18]

Koenemann, J., Belkin, N. J. A case for Interaction: A Study of Interactive Information Retrieval Behavior and Effectiveness in Proceedings of Proceedings of CHI '96 (New York 205--212.

Digital Library

[19]

Korfhage, R. R. 1997 Information storage and retrieval. Wiley Computer Publishing, N.Y.

Digital Library

[20]

Marchionini, G. Exploratory search: From finding to understanding, Communications of the ACM (2006) 49, 4, 41--46.

Digital Library

[21]

Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S. Personalized search on the World Wide Web, In: Brusilovsky, P., Kobsa, A., and Neidl, W., Eds. The Adaptive Web: Methods and Strategies of Web Personalization. Springer-Verlag, Berlin Heidelberg New York, 2007, 195--230.

Digital Library

[22]

Pazzani, M. J., Billsus, D. Content-based recommendation systems, In: Brusilovsky, P., Kobsa, A., and Neidl, W., Eds. The Adaptive Web: Methods and Strategies of Web Personalization. Springer-Verlag, Berlin Heidelberg New York, 2007, 325--341.

Digital Library

[23]

Robertson, S., Walker, S., and Hancock-Beaulieu, M. Okapi at TREC-7: automatic ad hoc, filtering, VLC and Interactive track. In Proceedings of the Seventh Text REtrieval Conference. Gaithersburg, USA, November 1998.

[24]

Rocchio Jr, J. J. Relevance feedback in information retrieval, In: Salton, G., Ed. The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice-Hall, Englewood Cliffs, NJ, USA, 1971, 313--323.

[25]

Ruthven, I., Lalmas, M. A survey on the use of relevance feedback for informaton access systems, The Knowledge Engineering Review (2003) 18, 2, 95--145.

Digital Library

[26]

Salojärvi, J., Puolamäki, K., Kaski, S. Implicit Relevance Feedback from Eye Movements in Proceedings of 15th International Conference on Artificial Neural Networks, ICANN 2005 (Warsaw, Poland, September 11--15, 2005), Springer-Verlag 513--518.

Digital Library

[27]

Waern, A. User involvement in automatic filtering - an experimental study, User Modeling and User Adapted Interaction (2004) 14, 201--237.

Digital Library

Cited By

Vakkari P(2023)Information Search Patterns in Complex TasksSRELS Journal of Information Management10.17821/srels/2023/v60i1/170892(19-30)Online publication date: 27-Mar-2023
https://doi.org/10.17821/srels/2023/v60i1/170892
Liu J(2021)Deconstructing search tasks in interactive information retrievalInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10252258:3Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1016/j.ipm.2021.102522
Vakkari PO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)The Usefulness of Search ResultsProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3377955(243-252)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3377955
Show More Cited By

Index Terms

Personalized web exploration with task models
1. Information systems
  1. Information retrieval
  2. World Wide Web
    1. Web applications
    2. Web services

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Published In

cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

Copyright © 2008 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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ACM: Association for Computing Machinery

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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: 21 April 2008

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WWW '08

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ACM

WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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

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Cited By

Vakkari P(2023)Information Search Patterns in Complex TasksSRELS Journal of Information Management10.17821/srels/2023/v60i1/170892(19-30)Online publication date: 27-Mar-2023
https://doi.org/10.17821/srels/2023/v60i1/170892
Liu J(2021)Deconstructing search tasks in interactive information retrievalInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10252258:3Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1016/j.ipm.2021.102522
Vakkari PO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)The Usefulness of Search ResultsProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3377955(243-252)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3377955
Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Liu JShah C(2019)Interactive IR User Study Design, Evaluation, and ReportingSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00923ED1V01Y201905ICR06711:2(i-75)Online publication date: 3-Jun-2019
https://doi.org/10.2200/S00923ED1V01Y201905ICR067
Sciascio CBrusilovsky PTrattner CVeas E(2019)A Roadmap to User-Controllable Social Exploratory SearchACM Transactions on Interactive Intelligent Systems10.1145/324138210:1(1-38)Online publication date: 30-Aug-2019
https://dl.acm.org/doi/10.1145/3241382
Wu DLiang S(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
https://doi.org/10.2200/S00831ED1V01Y201802ICR063
Ruotsalo TPeltonen JEugster MGłowacka DFloréen PMyllymäki PJacucci GKaski S(2018)Interactive Intent Modeling for Exploratory SearchACM Transactions on Information Systems10.1145/323159336:4(1-46)Online publication date: 3-Oct-2018
https://dl.acm.org/doi/10.1145/3231593
Koskela MLuukkonen PRuotsalo TSjÖberg MFloréen P(2018)Proactive Information Retrieval by Capturing Search Intent from Primary Task ContextACM Transactions on Interactive Intelligent Systems10.1145/31509758:3(1-25)Online publication date: 5-Jul-2018
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Wenskovitch JBradel LDowling MHouse LNorth C(2018)The Effect of Semantic Interaction on Foraging in Text Analysis2018 IEEE Conference on Visual Analytics Science and Technology (VAST)10.1109/VAST.2018.8802424(13-24)Online publication date: Oct-2018
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