A framework for long-term learning of topical user preferences in information retrieval

A framework for the long-term learning of user preferences in information retrieval is presented. The multiple indexing and method-object relations (MIMOR) model tightly integrates a fusion method and a relevance feedback processor into a learning model. Several black box matching functions can be c...

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Bibliographic Details
Published in:New library world 2004-01, Vol.105 (5/6), p.184-195
Main Authors: Mandl, Thomas, Womser-Hacker, Christa
Format: Article
Language:English
Subjects:
Algorithms
Cognition & reasoning
Collections
Computerized information storage and retrieval
Documents
Feedback
Indexing
Information retrieval
Information seeking behaviour
Information systems
Learning
Lifelong Learning
Machine learning
Matching
Mathematical analysis
Mathematical models
Microprocessors
MIMOR
Multiple Indexing and Method Object Relations
Neural networks
Optimization
Query expansion
Relevance feedback
Search engines
Searching
Soft computing
Studies
User behaviour
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Summary:A framework for the long-term learning of user preferences in information retrieval is presented. The multiple indexing and method-object relations (MIMOR) model tightly integrates a fusion method and a relevance feedback processor into a learning model. Several black box matching functions can be combined into a linear combination committee machine which reflects the user's vague individual cognitive concepts expressed in relevance feedback decisions. An extension based on the soft computing paradigm couples the relevance feedback processor and the matching function into a unified retrieval system.
ISSN:0307-4803
2398-5348
1758-6909
2398-5356
DOI:10.1108/03074800410536612