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Losee, Robert M.; Bookstein, Abraham – Information Processing and Management, 1988
Presents a model that places Boolean database queries into conjunctive normal form, thereby allowing probabilistic ranking of documents and the incorporation of relevance feedback. Experimental results compare the performance of a sequential learning probabilistic retrieval model with the proposed integrated Boolean probabilistic model and a fuzzy…
Descriptors: Feedback, Information Retrieval, Mathematical Models, Online Searching
Jansen, Bernard J.; Spink, Amanda; Saracevic, Tefko – 1999
This paper reports on a transaction log analysis of 51,473 queries from 18,113 users of Excite, a major World Wide Web search engine. Approximately 2,500 of these queries were from the use of relevance feedback. Given the high level of research activity and historical success of relevance feedback in assisting users in locating relevant…
Descriptors: Feedback, Information Retrieval, Relevance (Information Retrieval), Search Strategies

Harper, D. J.: Van Rijsbergen, C. J. – Journal of Documentation, 1978
Reports experiments with a term weighting model incorporating relevance information in which it is assumed that index terms are distributed dependently. It is argued that, if high recall searches are required, relevance feedback based on the modified dependence model may be superior to the Boolean search. (Author/VT)
Descriptors: Evaluation, Feedback, Information Retrieval, Library Research

Radecki, Tadeusz – Information Processing and Management, 1988
A theoretical framework used to derive a weighting mechanism for ranking output documents in Boolean retrieval systems is described in detail. A simple illustrative example is included, and the implications of such an approach are discussed. (64 references) (Author/CLB)
Descriptors: Feedback, Functions (Mathematics), Information Retrieval, Mathematical Models

Srinivasan, Padmini – Information Processing and Management, 1989
Describes rough sets theory and discusses the advantages it offers for information retrieval, including the implicit inclusion of Boolean logic, term weighting, ranked retrieval output, and relevance feedback. Rough set formalism is compared to Boolean, vector, and fuzzy models of information retrieval and a small scale evaluation of rough sets is…
Descriptors: Comparative Analysis, Feedback, Formative Evaluation, Information Retrieval

Gordon, Michael D. – Information Processing and Management, 1988
Describes the three subsystems of an information retrieval system (document descriptions, queries, and matching algorithms) and argues that the interdependency of these subsystems requires adaptation for the system to perform when any component changes. An algorithm for redescribing documents, in response to changes in queries and retrieval rules,…
Descriptors: Algorithms, Feedback, Information Retrieval, Models

Deogun, Jitender S.; Raghavan, Vijay V. – Information Processing and Management, 1988
Discusses the motivation for integrating information retrieval and database management systems, and proposes a probabilistic retrieval model in which records in a file may be composed of attributes (formatted data items) and descriptors (content indicators). The details and resolutions of difficulties involved in integrating such systems are…
Descriptors: Database Management Systems, Feedback, Information Retrieval, Mathematical Models

Fox, Edward A.; Koll, Matthew B. – Information Processing and Management, 1988
Provides an overview of methods for improving the effectiveness of Boolean retrieval systems, including document clustering, relevance feedback, weighted term searching, ranked output, and fuzzy set theory. The discussion of experimental studies with the SMART and SIRE systems focuses on the implementation and results of these methods. (69…
Descriptors: Bibliographic Databases, Comparative Analysis, Feedback, Full Text Databases