Emotion-oriented recommender system for personalized control of indoor environmental quality

Abstract

Indoor Environmental Quality (IEQ) significantly influences health, cognitive abilities, and even moods. With the rise of smart homes and the Internet of Things (IoT), the need for personalized IEQ control has become paramount. Traditional systems, primarily focusing on explicit feedback (i.e., preference), often neglect the role of emotions in shaping this feedback. Therefore, this study presents the Emotion-oriented Recommender System for personalized control of IEQ (ERS-IEQ), based on the R-E-C-S ontology, focusing on (i) Recognizing user's continuous emotional states, (ii) Estimating emotional similarity between users, (iii) Collecting user's feedback on IEQ conditions, and (iv) Systemizing an emotion-oriented recommender system using a graph neural network. In order to confirm the predictive performance of the ERS-IEQ and verify its higher level of excellence compared to traditional recommender systems, a private dataset composed of IEQ conditions, users' explicit feedback, and users' emotional states was built via a human participant experiment using a climate chamber. As a result, the ERS-IEQ significantly enhances the recommender system's predictive performance, particularly in thermal preferences. The number of linguistic terms of emotional similarity has a profound effect on the system's predictions, with four terms proving most effective. In the near future, ERS-IEQ will play a role as a personal assistant in smart home automation, offering emotion-based recommendations. It addresses key challenges in traditional recommender systems, such as the cold start problem and rating sparsity, and ensures personalized adjustments to IEQ conditions in both private and public spaces.