Enabling Performance Prediction in Information Retrieval Evaluation

How to model the performance of a retrieval system before its deploying has puzzled the Information Retrieval (IR) researchers for a long time. Currently, the evaluation of IR systems relies on empirical experiments. Empirical evaluation means that we need experimental collections: building them is expensive both in term of time and money. Exploiting already available collections to predict the performance of a system on new collections, would dramatically reduce such cost. With the research line described in this work, we plan to study the development of predictive models for the performance of the IR systems. In particular, the proposed research line will investigate Generalized Linear Mixed Models and Causal Inference. Furthermore, we highlight the importance of modelling the performance as distributions rather than point estimations.