Application of Markov chains in an interactive information retrieval system

摘要

The model of most information retrieval systems (IR) is based on matching an information seeker's expression of need (the query) to a document collection's representation of intellectual content and then presenting the retrieval set as a ranked hierarchical list (Baeza-Yates & Ribeiro-Neto, 1999, p. 28). This model has generated useful retrieval systems, but in the end is lacking essentially because it isolates information seeking behavior of the individual from how the resources are evaluated and applied within a social group, and clouds the user's interpretation of potential resources. While there is a rich literature of browsing, searching, and interface design, this paper proposes a Markov model of information retrieval (IR) that uses transition probabilities to guide information seeking behavior and group information seeking history to weight those probabilities. The whole was implemented in a Java application whose user interface reflects the underlying transition matrix. This paper details a Markov chain driven IR model. The motivation for this approach is based on potential weaknesses in traditional IR from the perspective of group awareness, query chains, and interactive visualization.