Skip to main content Skip to main navigation


The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction

Nicola Ferro; Norbert Fuhr; Gregory Grefenstette; Joseph A. Konstan; Pablo Castells; Elizabeth M. Daly; Thierry Declerck; Michael D. Ekstrand; Werner Geyer; Julio Gonzalo; Tsvi Kuflik; Krister Lindn; Bernardo Magnini; Jian-Yun Nie; Raffaele Perego; Bracha Shapira; Ian Soboroff; Nava Tintarev; Karin Verspoor; Martijn C. Willemsen; Justin Zobel
In: Claudia Hauff; Craig Macdonald (Hrsg.). SIGIR Forum, Vol. 52, No. 1, Pages 91-101, ACM, 6/2018.


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.