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 us music or movies to personal assistants able to 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. 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 the recent methodologies for building user models or developing personalized and adaptive systems.

Workshop on Explainable User Models and Personalised Systems (ExUM)

Musto C.;Polignano M.;Rapp A.;
2022-01-01

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 us music or movies to personal assistants able to 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. 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 the recent methodologies for building user models or developing personalized and adaptive systems.
2022
30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022
Barcellona
2022
UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
Association for Computing Machinery, Inc
160
162
9781450392327
Explainability; Explanations; Interpretability; Personalization; Recommender Systems; Transparency; User Models
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/2318/2096011
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