Relevance-Feedback-Based WWW Information Retrieval Considering Adatation to User's Behavior

Eguchi, Koichi; Ito, Hidetaka; Kumamoto, Akira

doi:10.1541/ieejeiss1987.117.11_1643

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…The constants c 1 of D and E are adjusted by Eqs. (2) and (3). Comparing the results of these two cases, we verified that the retrieval effectiveness was greatly improved by adjusting the parameters [11].…”

Section: Relevance Feedback With Adaptive Parameters Based On Documenmentioning

confidence: 58%

“…In this study, however, the following assumptions are made, in order to deal with vague information needs or the needs that may vary in the course of retrieval [2,3,7,8]. First, for relevance judgement:…”

Section: Relevance Feedback With Adaptive Parameters Based On Documenmentioning

confidence: 99%

“…In order to deal with such problems as (1) and (2), the authors have proposed a method [2,3] in which the conventional method of relevance feedback [1] is extended. To adapt to the users information needs that may vary with time, the feedback parameters are automatically adjusted (ARF: adaptive relevance feedback).…”

Section: Introductionmentioning

confidence: 99%

“…The parameters in Eqs (2). and(3)are empirically set as a = 0.679, b = 0.339, * Dp k,r 0 100 and Ep k,n 1 1[2,3,11].…”

mentioning

confidence: 99%

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Adaptive document clustering using incrementally expanded queries

et al. 2001

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This paper describes a method of increasing the effectiveness of browsing by clustering large numbers of information retrieval results, and discusses the implementation of adaptive document clustering. In the proposed method, query information is successively expanded and refined in the course of browsing, and it is utilized as a clustering viewpoint that expresses the interests of the user. The proposed method is experimentally evaluated in an application to WWW information retrieval and its effectiveness is demonstrated. © 2001 Scripta Technica, Syst Comp Jpn, 32(2): 6474, 2001

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Section: Relevance Feedback With Adaptive Parameters Based On Documenmentioning

confidence: 58%

Section: Relevance Feedback With Adaptive Parameters Based On Documenmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The parameters in Eqs (2). and(3)are empirically set as a = 0.679, b = 0.339, * Dp k,r 0 100 and Ep k,n 1 1[2,3,11].…”

mentioning

confidence: 99%

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Adaptive document clustering using incrementally expanded queries

et al. 2001

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“…It can be configured to automatically collect and summarize related objects from the Internet into a large collection or to collect, summarize, and handle a tiny specialized collection. Eguchi, Ito, and Kumamoto (1997) introduced a WWW retrieval system with an index-database architecture. The parameters in the standard Rocchio model (1971) are dynamically tuned according to the user's retrieval behaviors.…”

Section: Related Workmentioning

confidence: 99%

Web Discovery and Filtering Based on Textual Relevance Feedback Learning

Lam¹,

Wang²,

Yue³

2003

Computational Intelligence

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We develop a new approach for Web information discovery and filtering. Our system, called WID, allows the user to specify long-term information needs by means of various topic profile specifications. An entire example page or an index page can be accepted as input for the discovery. It makes use of a simulated annealing algorithm to automatically explore new Web pages. Simulated annealing algorithms possess some favorable properties to fulfill the discovery objectives. Information retrieval techniques are adopted to evaluate the content-based relevance of each page being explored. The hyperlink information, in addition to the textual context, is considered in the relevance score evaluation of a Web page. WID allows users to provide three forms of the relevance feedback model, namely, the positive page feedback, the negative page feedback, and the positive keyword feedback. The system is domain independent and does not rely on any prior knowledge or information about the Web content. Extensive experiments have been conducted to demonstrate the effectiveness of the discovery performance achieved by WID.

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