In this paper, we present the rationale and the ideas behind META4RS, a museum itinerary recommender system. The system leverages deep learning techniques to acquire data about the visitor’s position while ensuring her anonymity. Moreover, the visitor’s appraisal of the artwork she observes is inferred implicitly based on the emotional reactions she expresses while watching a given artwork. We are not aware of any such recommender system proposed in the research literature. However, this system should ensure several advantages: (i) it is non-intrusive since it makes use of simple badges and off-the-shelf cameras while ensuring the anonymity of the visitor; (ii) it is independent of the type of museum; (iii) it offers personalized itineraries to visitors based on their implicitly inferred interests and preferences. Specifically, we illustrate the background and describe the architecture of the proposed system, discussing the steps required for its implementation. We also provide details of what has already been done and what remains to be done, outlining the open problems.

Ferrato, A., Limongelli, C., Mezzini, M., Sansonetti, G. (2022). The META4RS Proposal: Museum Emotion and Tracking Analysis For Recommender Systems. In UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp.406-409) [10.1145/3511047.3537664].

The META4RS Proposal: Museum Emotion and Tracking Analysis For Recommender Systems

Ferrato A.;Limongelli C.;Mezzini M.;Sansonetti G.
2022

Abstract

In this paper, we present the rationale and the ideas behind META4RS, a museum itinerary recommender system. The system leverages deep learning techniques to acquire data about the visitor’s position while ensuring her anonymity. Moreover, the visitor’s appraisal of the artwork she observes is inferred implicitly based on the emotional reactions she expresses while watching a given artwork. We are not aware of any such recommender system proposed in the research literature. However, this system should ensure several advantages: (i) it is non-intrusive since it makes use of simple badges and off-the-shelf cameras while ensuring the anonymity of the visitor; (ii) it is independent of the type of museum; (iii) it offers personalized itineraries to visitors based on their implicitly inferred interests and preferences. Specifically, we illustrate the background and describe the architecture of the proposed system, discussing the steps required for its implementation. We also provide details of what has already been done and what remains to be done, outlining the open problems.
9781450392327
Ferrato, A., Limongelli, C., Mezzini, M., Sansonetti, G. (2022). The META4RS Proposal: Museum Emotion and Tracking Analysis For Recommender Systems. In UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp.406-409) [10.1145/3511047.3537664].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/415227
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