• Nicola Ferro
  • Norbert Fuhr
  • Gregory Grefenstette
  • Tsvi Kuflik
  • Krister Lindén
  • Bernardo Magnini
  • Jian-Yun Nie
  • Raffaele Perego
  • Nava Tintarev
  • More Authors
We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying 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)96-139
Number of pages44
JournalDagstuhl Manifestos
Issue number1
Publication statusPublished - 2019
EventPerspectives Workshop - Schloss Dagatuhl, Wadern, Germany
Duration: 30 Oct 20193 Nov 2019

ID: 47509620