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. WillemsenJustin Zobel

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Pages (from-to)91-101
Number of pages11
JournalSIGIR Forum
Volume52
Issue number1
DOIs
Publication statusPublished - 1 Aug 2018

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