Article

The re:search engine: simultaneous support for finding and re-finding

Author:

Jaime TeevanAuthors Info & Claims

UIST '07: Proceedings of the 20th annual ACM symposium on User interface software and technology

Pages 23 - 32

https://doi.org/10.1145/1294211.1294217

Published: 07 October 2007 Publication History

Abstract

Re-finding, a common Web task, is difficult when previously viewed information is modified, moved, or removed. For example, if a person finds a good result using the query "breast cancer treatments", she expects to be able to use the same query to locate the same result again. While re-finding could be supported by caching the original list, caching precludes the discovery of new information, such as, in this case, new treatment options. People often use search engines to simultaneously find and re-find information. The Re:Search Engine is designed to support both behaviors in dynamic environments like the Web by preserving only the memorable aspects of a result list. A study of result list memory shows that people forget a lot. The Re:Search Engine takes advantage of these memory lapses to include new results where old results have been forgotten.

References

[1]

Ahlström, D. (2005). Modeling and improving selection in cascading pull-down menus using Fitts' law, the steering law and force fields. In Proceedings of CHI '05, 61--70.

Digital Library

[2]

Aula, A., Jhaveri, N., and Käki, M. (2005). Information search and reaccess strategies of experienced Web users. In Proceedings of WWW '05, 583--592.

Digital Library

[3]

Bruce, H., Jones, W. and Dumais, S. (2004). Keeping and refinding information on the Web: What do people do and what do they need?allIn Proceedings of ASIST '04.

[4]

Capra, R. and Pérez-Quiñones, M.A. (2005). Using Web search engines to find and refind information. IEEE Computer, 38 (10), 36--42.

Digital Library

[5]

Dumais, S. T., Cutrell, E., Cadiz, J. J., Jancke, G., Sarin, R. and Robbins, D. C. (2003). Stuff I've Seen: A system for personal information retrieval and re-use. In Proceedings of SIGIR '03, 72--79.

Digital Library

[6]

Durlach, P. J. (2004). Change blindness and its implications for complex monitoring and control systems design and operator training. Human-Computer Interaction, 19(4): 423--451.

Digital Library

[7]

Goldberg, A.V. (1997). An efficient implementation of a scaling minimum-cost flow algorithm. Journal of Algorithms, 22(1): 1--29.

Digital Library

[8]

Hayashi, K., Nomura, T., Hazama, T., Takeoka, M., Hashimoto, S., and Gudmundson, S. (1998). Temporally-threaded workspace: A model for providing activity-based perspectives on document spaces. In Proceeding of HyperText '98.

Digital Library

[9]

Komlodi, A., Soergel, D., and Marhionini, G. (2006). Search histories for user support in user interfaces. JASIST, 57(6): 803--807.

Digital Library

[10]

Levy, D. (1994). Fixed or fluid? Document stability and new media. In Proceedings of European Conference on Hypertext.

Digital Library

[11]

Mitchell, J. and Shneiderman, B. (1989). Dynamic versus static menus: An exploratory comparison. ACM SIGCHI Bulletin, 20(4): 33--37.

Digital Library

[12]

Murdock, B. B. (1962). The Serial Position Effect of free recall. Journal of Experimental Psychology, 64, 482--488.

[13]

Nowell, L., Hetzler, E., and Tanasse, T. (2001). Change blindness in information visualization: A case study. In Proceedings of INFOVIS '01, 15--22.

Digital Library

[14]

Obendorf, H., Weinreich, H., Herder, E., and Mayer, M. (2007). Web page revisitation revisited: Implications of a long-term click-stream study of browser usage. In Proceedings of CHI '07, 597--606.

Digital Library

[15]

Rainie, L. and Shermak, J. (2005). Pew Internet and American Life Project:allData memo on search engine use. Retrieved January, 2006 from http://www.pewinternet.org/pdfs/PIP_SearchData_1105.pdf.

[16]

Rekimoto, J. (1999). Time-machine computing: A time-centric approach for the information environment. In Proceedings of UIST '99, 45--54.

Digital Library

[17]

Rubin, R. C. and Wenzel, A. E. (1996). 100 years of forgetting: A quantitative description of Retention. Psychological Review, 103, 734--760.

[18]

Salton, G. (1998). Automatic text indexing using complex identifiers. In Proceedings of the ACM conference on Document processing systems, 135--144.

Digital Library

[19]

Selberg, E. and Etzioni, O. (2000). On the instability of Web search engines. In Proceedings of RIAO '00.

[20]

Silverstein, C., Marais, H., Henzinger, M., and Moricz, M. (1999). Analysis of a very large Web search engine query log. ACM SIGIR Forum, 33(1): 6--12.

Digital Library

[21]

Simons, D. J. and Rensink, R. A. (2005). Change blindness: Past, present, and future. Trends in Cognitive Sciences, 9(1):16--20.

[22]

Somberg, B. L. (1986). A comparison of rule-based and positionally constant arrangements of computer menu items. In Proceedings of CHI/GI '86, 255--260.

Digital Library

[23]

Teevan, J. (2006). How people recall search result lists. In Proceedings of CHI '06, 1415--1420.

Digital Library

[24]

Teevan, J. (2007). Supporting finding and refinding through personalization. Doctoral thesis, Massachusetts Institute of Technology.

Digital Library

[25]

Teevan, J., Adar, E., Jones, R., and Potts, M. (2007). Information Re-Retrieval: Repeat queries in Yahoo's logs. In Proceedings of SIGIR '07.

Digital Library

[26]

Teevan, J., Alvarado, C., Ackerman, M. S., and Karger, D. R. (2004). The perfect search engine is not enough: A study of orienteering behavior in directed search. In Proceedings of CHI '04, 415--422.

Digital Library

[27]

Tognazzini (1999). A quiz designed to give you Fitts. http://asktog.com/columns/022DesignedToGiveFitts.html

[28]

Varakin, D. A., Levin, D. T., and Fidler, R. (2004). Unseen and unaware: Implications of recent research on failures of visual awareness for human-computer interface design. Human-Computer Interaction, 19(4): 389--422.

Digital Library

[29]

White, R., Ruthven, I., and Jose, J.M. (2002). Finding relevant documents using top ranking sentences: An evaluation of two alternative schemes. In Proceedings of SIGIR '02, 57--64.

Digital Library

Cited By

Liu BLu XCulpepper J(2021)Strong natural language query generationInformation Retrieval Journal10.1007/s10791-021-09395-3Online publication date: 15-Jul-2021
https://doi.org/10.1007/s10791-021-09395-3
Meier FElsweiler DShah CBelkin NByström KHuang JScholer F(2018)Other Times Itźs Just Strolling Back Through My TimelineProceedings of the 2018 Conference on Human Information Interaction & Retrieval10.1145/3176349.3176392(130-139)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1145/3176349.3176392
ud Din IKhusro SUllah IRauf A(2018)Semantic History: Ontology-Based Modeling of Users’ Web Browsing Behaviors for Improved Web Page RevisitationIntelligent Systems in Cybernetics and Automation Control Theory10.1007/978-3-030-00184-1_19(204-215)Online publication date: 29-Aug-2018
https://doi.org/10.1007/978-3-030-00184-1_19
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Index Terms

The re:search engine: simultaneous support for finding and re-finding
1. Applied computing
  1. Document management and text processing
    1. Document preparation
      1. Multi / mixed media creation
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Graphical user interfaces
      2. Hypertext / hypermedia

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cover image ACM Conferences

UIST '07: Proceedings of the 20th annual ACM symposium on User interface software and technology

October 2007

306 pages

ISBN:9781595936790

DOI:10.1145/1294211

General Chairs:
Chia Shen
MERL, USA
,
Robert Jacob
Tufts University, USA
,
Program Chair:
Ravin Balakrishnan
University of Toronto, Canada

Copyright © 2007 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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Published: 07 October 2007

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UIST07: The 20th Annual ACM Symposium on User Interface Software and Technology

October 7 - 10, 2007

Rhode Island, Newport, USA

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Overall Acceptance Rate 561 of 2,567 submissions, 22%

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Cited By

Liu BLu XCulpepper J(2021)Strong natural language query generationInformation Retrieval Journal10.1007/s10791-021-09395-3Online publication date: 15-Jul-2021
https://doi.org/10.1007/s10791-021-09395-3
Meier FElsweiler DShah CBelkin NByström KHuang JScholer F(2018)Other Times Itźs Just Strolling Back Through My TimelineProceedings of the 2018 Conference on Human Information Interaction & Retrieval10.1145/3176349.3176392(130-139)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1145/3176349.3176392
ud Din IKhusro SUllah IRauf A(2018)Semantic History: Ontology-Based Modeling of Users’ Web Browsing Behaviors for Improved Web Page RevisitationIntelligent Systems in Cybernetics and Automation Control Theory10.1007/978-3-030-00184-1_19(204-215)Online publication date: 29-Aug-2018
https://doi.org/10.1007/978-3-030-00184-1_19
Hwang HHerder ERonchetti MPaternò FSpano LArdito CSantoro C(2017)A Link Back to MemoryLaneProceedings of the 12th Biannual Conference on Italian SIGCHI Chapter10.1145/3125571.3125586(1-10)Online publication date: 18-Sep-2017
https://dl.acm.org/doi/10.1145/3125571.3125586
Seifert CSchlotterer JGranitzer M(2017)QueryCrumbs: A Compact Visualization for Navigating the Search Query History2017 21st International Conference Information Visualisation (IV)10.1109/iV.2017.23(35-44)Online publication date: Jul-2017
https://doi.org/10.1109/iV.2017.23
Jin LFeng LLiu GWang C(2017)Personal Web Revisitation by Context and Content Keywords with Relevance FeedbackIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.267274729:7(1508-1521)Online publication date: 1-Jul-2017
https://doi.org/10.1109/TKDE.2017.2672747
Do TRuddle R(2017)MyWebSteps: Aiding Revisiting with a Visual Web HistoryInteracting with Computers10.1093/iwc/iww038Online publication date: 13-Jan-2017
https://doi.org/10.1093/iwc/iww038
Li Xde Rijke M(2017)Do Topic Shift and Query Reformulation Patterns Correlate in Academic Search?Advances in Information Retrieval10.1007/978-3-319-56608-5_12(146-159)Online publication date: 8-Apr-2017
https://doi.org/10.1007/978-3-319-56608-5_12
Han SHe DChi Y(2017)Understanding and modeling behavior patterns in cross‐device web searchProceedings of the Association for Information Science and Technology10.1002/pra2.2017.1450540101754:1(150-158)Online publication date: 24-Oct-2017
https://doi.org/10.1002/pra2.2017.14505401017
Meier FElsweiler DPerego RSebastiani FAslam JRuthven IZobel J(2016)Going back in TimeProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911524(355-364)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2911524
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