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.
Information retrieval failure analysis: Visual analytics as a support for interactive "what-if" investigation / Angelini, Marco; Ferro, NICOLA MARIA; Granato, GUIDO LORENZO; Silvello, Gianmaria; Santucci, Giuseppe. - STAMPA. - (2012), pp. 205-206. (Intervento presentato al convegno 2012 IEEE Conference on Visual Analytics Science and Technology, VAST 2012 tenutosi a Seattle, WA, usa nel 2012) [10.1109/VAST.2012.6400551].
Information retrieval failure analysis: Visual analytics as a support for interactive "what-if" investigation
ANGELINI, MARCO;FERRO, NICOLA MARIA;GRANATO, GUIDO LORENZO;SANTUCCI, Giuseppe
2012
Abstract
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.| File | Dimensione | Formato | |
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