User modelling using evolutionary interactive reinforcement learning

Abstract

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.