A Reinforcement Learning Strategy for (formal) Concept and Keyword Weight Learning for Adaptive Information Retrieval

This paper reports our experimental investigation into the use of a reinforcement learning strategy to learn weights of (formal) concepts and keywords to support Information Retrieval. This work is a part of our main research objective of using more elegant construct of a concept rather than simple keywords as the basic element of representation and matching. The framework used for achieving this was based on the theory of Formal Concept Analysis (FCA) and Lattice theory. Features or concepts (formulated according to FCA) of each document (and query) are represented in a separate concept lattice and are weighted separately with respect to the document. The document retrieval process is viewed as a continuous conversation between queries and documents, during which documents are allowed to learn a consistent set of significant concepts to help their retrieval. The learning strategy used was based on relevance feedback information that makes the similarity of relevant documents stronger and nonrelevant documents weaker. Test results obtained on the Cranfield collection show a significant increase of average precisions as the system gains more experience.