The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction
Nicola Ferro (University of Padua)
Norbert Fuhr (External organisation)
Gregory Grefenstette (External organisation)
Joseph A. Konstan (Association for Computing Machinery (ACM))
Pablo Castells (External organisation)
Elizabeth M. Daly (External organisation)
Thierry Declerck (External organisation)
Michael D. Ekstrand (Boise State University)
Nava Tintarev (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
This paper reports the findings of the Dagstuhl Perspectives Workshop 17442 on performance modeling and prediction in the domains of Information Retrieval, Natural language Processing and Recommender Systems. We present a framework for further research, which identifies five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performance.