JOURNAL ARTICLE

Adaptive relevance feedback in information retrieval

Abstract

Relevance Feedback has proven very effective for improving retrieval accuracy. A difficult yet important problem in all relevance feedback methods is how to optimally balance the original query and feedback information. In the current feedback methods, the balance parameter is usually set to a fixed value across all the queries and collections. However, due to the difference in queries and feedback documents, this balance parameter should be optimized for each query and each set of feedback documents.

Keywords:
Relevance feedback Computer science Relevance (law) Set (abstract data type) Information retrieval Value (mathematics) Balance (ability) Data mining Artificial intelligence Machine learning Image retrieval

Metrics

113
Cited By
28.61
FWCI (Field Weighted Citation Impact)
40
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Information Retrieval and Search Behavior
Physical Sciences →  Computer Science →  Information Systems
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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