In this paper we point out some relevant issues in relation to privacy when providing holistic recommendations. We emphasize that a holistic recommender should be fair, explainable and privacy-preserving to ensure the ethicality of the recommendation process. Further, we point out relevant research questions that should be addressed in the future, as well as propose some preliminary suggestions to face the emergent issues with reference to privacy in the recommendation domain.

Privacy issues in holistic recommendations

Cena F.;Pensa R. G.;Rapp A.
2019-01-01

Abstract

In this paper we point out some relevant issues in relation to privacy when providing holistic recommendations. We emphasize that a holistic recommender should be fair, explainable and privacy-preserving to ensure the ethicality of the recommendation process. Further, we point out relevant research questions that should be addressed in the future, as well as propose some preliminary suggestions to face the emergent issues with reference to privacy in the recommendation domain.
2019
Explainable and Holistic User Modeling (ExHUM 2019) Workshop, co-located with the 27th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2019
Larnaca, Cyprus
June 9, 2019
ACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
Association for Computing Machinery, Inc
263
265
9781450367110
http://dl.acm.org/citation.cfm?id=3314183
Context-aware recommender systems; Holistic User Model; Privacy; Recommender Systems; Ubiquitous Context
Cena F.; Pensa R.G.; Rapp A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1714026
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