Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interacting with algorithms that help us in several scenarios, ranging from services that suggest music or movies to personal assistants who proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the EU General Data Protection Regulation (GDPR) emphasized the users’ right to explanation when people face intelligent systems. Unfortunately, current research tends to go in the opposite direction since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of model explainability. The workshop aims to provide a forum for discussing problems, challenges, and innovative research approaches in this area by investigating the role of transparency and explainability in recent methodologies for building user models or developing personalized and adaptive systems.

5th Workshop on Explainable User Models and Personalised Systems (ExUM)

Musto C.;Rapp A.;Semeraro G.;
2023-01-01

Abstract

Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interacting with algorithms that help us in several scenarios, ranging from services that suggest music or movies to personal assistants who proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the EU General Data Protection Regulation (GDPR) emphasized the users’ right to explanation when people face intelligent systems. Unfortunately, current research tends to go in the opposite direction since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of model explainability. The workshop aims to provide a forum for discussing problems, challenges, and innovative research approaches in this area by investigating the role of transparency and explainability in recent methodologies for building user models or developing personalized and adaptive systems.
2023
The 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP '23)
Limassol, Cipro
26-29 Giugno 2023
Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP '23 Adjunct)
ACM
196
198
9781450398916
Explainability, Transparency, Interpretability, User Modeling, Personalization, Recommender Systems
Musto C.; Delic A.; Inel O.; Polignano M.; Rapp A.; Semeraro G.; Ziegler J.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1947177
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