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Privacy Dashboards: The Impact of the Type of Personal Data and User Control on Trust and Perceived Risk

Autor: Herder, E., van Maaren, O., Kuflik, T.

Publikováno v: UMAP (Adjunct Publication)
Kuflik, T. (ed.), UMAP '20: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, pp. 169-174
Kuflik, T. (ed.), UMAP '20: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 169-174. New York : Association for Computing Machinery
STARTPAGE=169;ENDPAGE=174;TITLE=Kuflik, T. (ed.), UMAP '20: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Website owners often collect personal data to, among other things, advertise more efficiently and to analyze and increase sales. They can inform users in various ways about what data they collect and how they process it. This study focuses on the use

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::563fe32e797563325599d77240d1be64
https://doi.org/10.1145/3386392.3399557

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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)

Publikováno v: 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

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 r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1750191cbfef215aabc9b6e34ddd7823
http://resolver.tudelft.nl/uuid:f07a881f-056f-46aa-8686-30597f174442

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