Embedding Emotional Context in Recommender Systems

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Emotional context is becoming a promising paradigm to develop more intuitive and sensitive recommender systems. Ambient Recommender Systems, arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We explain some results of these new trends in real-world applications through the Smart Prediction Assistant (SPA) platform implemented in an Intelligent Learning Guide. While most approaches to recommending focus 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 the SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world recommender systems