Improving Information Retrieval Evaluation via Markovian User Models and Visual Analytics

To address the challenge of adapting experimental evaluation to the constantly evolving user tasks and needs, we develop a new family of Markovian Information Retrieval (IR) evaluation measures, called Markov Precision (MP), where the interaction between the user and the ranked result list is modelled via Markov chains, and which will be able to explicitly link lab-style and on-line evaluation methods. Moreover, since experimental results are often not so easy to understand, we will develop a Web-based Visual Analytics (VA) prototype where an animated state diagram of the Markov chain will explain how the user is interacting with the ranked result list in order to offer a support for a careful failure analysis.