Weighted Markov Chains and Graphic State Nodes for Information Retrieval

Abstract

Decision-making in uncertain environments, such as data mining, involves a computer user navigating through multiple steps, from initial submission of a query through evaluating retrieval results, determining degrees of acceptability of the results, and advancing to a terminal state of evaluating where the interaction is successful or not. This paper describes iterative information seeking (IS) as a Markov process during which users advance through states of "nodes". Nodes are graphic objects on a computer screen that represent both the state of the system and the group of users' or an individual user's degree of confidence in an individual node. After examining nodes to establish a confidence level, the system records the decision as weights affecting the probability of the transition paths between nodes. By training the system in this way, the model incorporates into the underlying Markov process users' decisions as a means to reduce uncertainty. The Markov chain becomes a weighted one whereby the IS makes justified suggestions.