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Journal article
From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442): Manifesto from Dagstuhl Perspectives Workshop 17442
Dagstuhl Manifestos, v 7(1)
01 Jan 2018
Abstract
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 predic- tion models describing the relationship between assumptions, features and resulting performance
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Details
- Title
- From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)
- Creators
- Ferro N.Fuhr N.Grefenstette G.Konstan J. A.Castells P.Daly E. M.Declerck T.Ekstrand M. D.Geyer W.Gonzalo J.Kuflik T.Lind'En K.Magnini B.Nie J. Y.Perego R.Shapira B.Soboroff I.Tintarev N.Verspoor K.Willemsen M. C.Zobel J.
- Publication Details
- Dagstuhl Manifestos, v 7(1)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991021818498604721