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From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442). / Ferro, Nicola; Fuhr, Norbert; Grefenstette, Gregory; Kuflik, Tsvi; Lindén, Krister; Magnini, Bernardo; Nie, Jian-Yun; Perego, Raffaele; Tintarev, Nava; More Authors.

In: Dagstuhl Manifestos, Vol. 7, No. 1, 2019, p. 96-139.

Research output: Contribution to journalArticleScientific

Harvard

Ferro, N, Fuhr, N, Grefenstette, G, Kuflik, T, Lindén, K, Magnini, B, Nie, J-Y, Perego, R, Tintarev, N & More Authors 2019, 'From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)', Dagstuhl Manifestos, vol. 7, no. 1, pp. 96-139. https://doi.org/10.4230/DagMan.7.1.96

APA

Vancouver

Author

Ferro, Nicola ; Fuhr, Norbert ; Grefenstette, Gregory ; Kuflik, Tsvi ; Lindén, Krister ; Magnini, Bernardo ; Nie, Jian-Yun ; Perego, Raffaele ; Tintarev, Nava ; More Authors. / From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442). In: Dagstuhl Manifestos. 2019 ; Vol. 7, No. 1. pp. 96-139.

BibTeX

@article{f07a881f056f46aa868630597f174442,
title = "From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)",
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 prediction models describing the relationship between assumptions, features and resulting performance. ",
author = "Nicola Ferro and Norbert Fuhr and Gregory Grefenstette and Tsvi Kuflik and Krister Lind{\'e}n and Bernardo Magnini and Jian-Yun Nie and Raffaele Perego and Nava Tintarev and {More Authors}",
year = "2019",
doi = "10.4230/DagMan.7.1.96",
language = "English",
volume = "7",
pages = "96--139",
journal = "Dagstuhl Manifestos",
issn = "2193-2433",
publisher = "Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany",
number = "1",
note = "Perspectives Workshop ; Conference date: 30-10-2019 Through 03-11-2019",

}

RIS

TY - JOUR

T1 - From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)

AU - Ferro, Nicola

AU - Fuhr, Norbert

AU - Grefenstette, Gregory

AU - Kuflik, Tsvi

AU - Lindén, Krister

AU - Magnini, Bernardo

AU - Nie, Jian-Yun

AU - Perego, Raffaele

AU - Tintarev, Nava

AU - More Authors, null

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.4230/DagMan.7.1.96

DO - 10.4230/DagMan.7.1.96

M3 - Article

VL - 7

SP - 96

EP - 139

JO - Dagstuhl Manifestos

JF - Dagstuhl Manifestos

SN - 2193-2433

IS - 1

T2 - Perspectives Workshop

Y2 - 30 October 2019 through 3 November 2019

ER -

ID: 47509620