The classical Probability Ranking Principle (PRP) forms the theoretical basis for probabilistic Information Retrieval (IR) models, which are dominating IR theory since about 20 years. However, the assumptions underlying the PRP often do not hold, and its view is too narrow for interactive information retrieval (IIR). In this paper, a new theoretical framework for interactive retrieval is proposed: The basic idea is that during IIR, a user moves between situations. In each situation, the system presents to the user a list of choices, about which s/he has to decide, and the first positive decision moves the user to a new situation. Each choice is associated with a number of cost and probability parameters. Based on these parameters, an optimum ordering of the choices can the derived- the PRP for IIR. The relationship of this rule to the classical PRP is described, and issues of further research are pointed out. 1

Abstract. This paper introduces PIRE, a probabilistic IR engine. For both document indexing and retrieval, PIRE makes heavy use of probabilistic Datalog, a probabilistic extension of predicate Horn logics. Using such a logical framework together with probability theory allows for defining and using data types (e.g. text, names, numbers), different weighting schemes (e.g. normalised tf, tf.idf or BM25) and retrieval functions (e.g. uncertain inference, language models). Extending the system thus is reduced to adding new rules. Furthermore, this logical framework provide a powerful tool for including additional background knowledge into the retrieval process. 1

Image retrieval has become an interesting and active field due to the increasing necessity of searching and browsing very large image repositories. Images are represented using several kinds of low-level descriptors from which convenient similarity or score functions are computed. Recent work deals with different ways of combining these measures to improve the overall performance of the retrieval system. This paper builds upon previous ideas taken from different contexts to deploy a convenient combination framework that takes into account learning data directly gathered from the users that are supposed to end using the system. The proposal is empirically evaluated and compared to other ways of combining the same measures. 1.