Information retrieval failure analysis: Visual analytics as a support for interactive “what-if” investigation

This poster provides an analytical model for examining performances of IR systems, based on the discounted cumulative gain family of metrics, and visualization for interacting and exploring the performances of the system under examination. Moreover, we propose machine learning approach to learn the ranking model of the examined system in order to be able to conduct a “what-if” analysis and visually explore what can happen if you adopt a given solution before having to actually implement it.