article

The FindMe Approach to Assisted Browsing

Authors:

Robin D. Burke,

Kristian J. Hammond,

Benjamin C. YoungAuthors Info & Claims

IEEE Expert: Intelligent Systems and Their Applications, Volume 12, Issue 4

Pages 32 - 40

https://doi.org/10.1109/64.608186

Published: 01 July 1997 Publication History

Abstract

While the explosion of on-line information has introduced new opportunities for finding and using electronic data, it has also underscored the problem of isolating useful information and making sense of large, multidimensional information spaces. In response to this problem, we have developed an approach to building data tour guides, called FindMe systems. These programs know enough about an information space to help users navigate through it, making sure they not only come away with useful information but also insights into the structure of the information space itself.In these systems, we have combined ideas of instance-based browsing, which involves structuring retrieval around the critiquing of previously retrieved examples, and retrieval strategies, or knowledge-based heuristics for finding relevant information. This article illustrates these techniques with examples of working FindMe systems, and describes the similarities and differences between them. each item to consider, such selection tasks typically require substantial knowledge to perform well. Our aim is to build systems that can help users perform such tasks, even when they do not have much specific knowledge. Our approach, called assisted browsing, combines searching and browsing with knowledge-based assistance.

References

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Digital Library

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D.R. Cutting, et al., "Scatter/Gather: A Cluster-Based Approach to Browsing Large Document Collections," Proc. 15th Ann. Int'l ACM/SIGIR Conf., ACM Press, New York, 1992, pp. 318-329.

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J.D. Ullman, Principles of Database and Knowledge-Base Systems, Vol. 1, Computer Science Press, Potomac, Md., 1988.

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M.D. Williams, et al., "RABBIT: Cognitive Science in Interface Design," Fourth Ann. Conf. Cognitive Science Soc., Lawrence Erlbaum and Associates, New York, 1982, pp. 82-85.

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Cited By

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Rana ASanner SBouadjenek MDicarlantonio RFarmaner G(2023)User Experience and The Role of Personalization in Critiquing-Based Conversational RecommendationACM Transactions on the Web10.1145/3597499Online publication date: 18-May-2023
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cover image IEEE Expert: Intelligent Systems and Their Applications

IEEE Expert: Intelligent Systems and Their Applications Volume 12, Issue 4

July 1997

88 pages

ISSN:0885-9000

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Copyright © Copyright © 1997 IEEE. All Rights Reserved.

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IEEE Educational Activities Department

United States

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Published: 01 July 1997

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Cited By

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https://dl.acm.org/doi/10.1145/3627043.3659565
Rana ASanner SBouadjenek MDicarlantonio RFarmaner G(2023)User Experience and The Role of Personalization in Critiquing-Based Conversational RecommendationACM Transactions on the Web10.1145/3597499Online publication date: 18-May-2023
https://dl.acm.org/doi/10.1145/3597499
Behera GNain N(2023)The State-of-the-Art and Challenges on Recommendation System’s: Principle, Techniques and Evaluation StrategySN Computer Science10.1007/s42979-023-02207-z4:5Online publication date: 3-Sep-2023
https://dl.acm.org/doi/10.1007/s42979-023-02207-z
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