Publikation
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.
Zusammenfassung
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.