Beyond the keyword barrier: knowledge-based information retrieval
Information Services and Use
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A re-examination of relevance: toward a dynamic, situational definition
Information Processing and Management: an International Journal
Cognitive process as a basis for intelligent retrieval systems design
Information Processing and Management: an International Journal
The SMART and SIRE experimental retrieval systems
Readings in information retrieval
Journal of the American Society for Information Science
Measures of relative relevance and ranked half-life: performance indicators for interactive IR
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Crossover improvement for the genetic algorithm in information retrieval
Information Processing and Management: an International Journal
A distance and angle similarity measure method
Journal of the American Society for Information Science
Overview of the sixth text REtrieval conference (TREC-6)
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
Evaluating evaluation measure stability
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Knowledge-Based and Statistical Approaches to Text Retrieval
IEEE Expert: Intelligent Systems and Their Applications
The concept of relevance in IR
Journal of the American Society for Information Science and Technology
Toward new communication paradigms to enhance cognitive and learning processes
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems
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As the volume and variety of information sources continues to grow, there is increasing difficulty with respect to obtaining information that accurately matches user information needs. A number of factors affect information retrieval effectiveness (the accuracy of matching user information needs against the retrieved information). First, users often do not present search queries in the form that optimally represents their information need. Second, the measure of a document's relevance is often highly subjective between different users. Third, information sources might contain heterogeneous documents, in multiple formats and the representation of documents is not unified. This paper discusses an approach for improvement of information retrieval effectiveness from document databases. It is proposed that retrieval effectiveness can be improved by applying computational intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative (subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An information retrieval is developed whose retrieval effectiveness is evaluated using traditional precision and recall.