From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)
| Autor: | 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. |
|---|---|
| Přispěvatelé: | Human Technology Interaction |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
000 Computer science
knowledge general works 05 social sciences Formal models User interaction 02 engineering and technology 020204 information systems Computer Science 0202 electrical engineering electronic engineering information engineering 0509 other social sciences Evaluation 050904 information & library sciences Simulation Information Systems |
| Zdroj: | Dagstuhl Manifestos, 7(1) Dagstuhl Manifestos, 7(1), 96-139. Schloss Dagstuhl-Leibniz-Zentrum für Informatik Dagstuhl manifestos 7 (2018): 96–139. doi:10.4230/DagMan.7.1.96 info:cnr-pdr/source/autori: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./titolo:From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)/doi:10.4230%2FDagMan.7.1.96/rivista:Dagstuhl manifestos/anno:2018/pagina_da:96/pagina_a:139/intervallo_pagine:96–139/volume:7 |
| ISSN: | 2193-2433 |
| DOI: | 10.4230/DagMan.7.1.96 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|