Title :
Information retrieval failure analysis: Visual analytics as a support for interactive “what-if” investigation
Author :
Angelini, M. ; Ferro, N. ; Granato, G. ; Santucci, G. ; Silvello, G.
Author_Institution :
Sapienza Univ. of Roma, Rome, Italy
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.
Keywords :
data analysis; data visualisation; information retrieval; learning (artificial intelligence); IR systems; analytical model; discounted cumulative gain metric family; information retrieval failure analysis; interactive what-if investigation; machine learning approach; ranking model; visual analytics; what-if analysis; Analytical models; Educational institutions; Failure analysis; Image color analysis; Information retrieval; Prototypes; Visual analytics;
Conference_Titel :
Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4673-4752-5
DOI :
10.1109/VAST.2012.6400551
