Embedding Emotional Context in Recommender Systems
Abstract
Emotions are crucial for user's decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems.
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Affective recommender systems use techniques such as natural language processing to make inferences about users' emotional states and even combine them with other context information such as the recommendation domain.
“Affective recommender systems 129 use techniques such as natural language processing to make inferences about users' emotional states and even combine them with other context information such as the recommendation domain (for example, music or movies) [130][131][132] .”
From Machine culture (2023) · cites this paper as [131]
Cites
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Brinkmann, L., Baumann, F., Bonnefon, J.-F., Derex, M., Müller, T. F., Nussberger, A.-M., Czaplicka, A., Acerbi, A., Griffiths, T. L., Henrich, J., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01742-2 · Cited by 132 · see citing works
- Authors
- Gonzalez, G., de la Rosa, J. L., Montaner, M., & Delfin, S.
- Affiliations
- Universitat de Girona (ES)
- Venue
- 2007 IEEE 23rd International Conference on Data Engineering Workshop
- Publisher
- IEEE
- Published
- 2007-04
- Pages
- 845-852
- OpenAlex
- W2153631993
- Cited by
- 64
- References
- 10
- Citations by year
- 2025: 3 · 2023: 2 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 4 · 2018: 4 · 2017: 3 · 2016: 3 · 2015: 7 · 2014: 9 · 2013: 5 · 2012: 5
- FWCI
- 2.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Data Engineering Workshops (ICDEW) (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion)
- Universitat de Girona Digital Repository (Universitat de Girona) (submittedVersion)
- Repositori UJI (Universitat Jaume I) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- recommender systems, emotional context, emotion-aware recommendation, technology transfer, user decision-making
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