The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction

Journal article (2018)

Authors

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
N. Tintarev Web Information Systems - EEMCS
More Authors More Authors External organisation
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2018 Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, N. Tintarev, More Authors
DOI
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https://doi.org/10.1145/3274784.3274789
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

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.