ברכה שפירא

אקדמי בכיר

From Evaluating to Forecasting Performance

How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442).

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 Lindén, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, Justin Zobel
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.
שפת פרסום אנגלית
דפים 96-139
כתב עת Dagstuhl Manifestos
כרך 7
נושא מספר 1
סטטוס פרסום פורסם - 11.12.2018
מספר מאמר 1
גישה למסמך
10.4230/DagMan.7.1.96 https://dblp.org/db/journals/dagstuhl-manifestos/dagstuhl-manifestos7.html#FerroFGKCDDEGGK18