Personalized Prediction of Indoor Comfort Using Graph Convolutional Matrix Completion
Personalized Prediction of Indoor Comfort Using Graph Convolutional Matrix Completion
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DOI：
10.1109/mipr54900.2022.00053
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Junyi Liu;E. Naidu;Jialian Wu;Shira Gabriel;E. Steinfeld;Junsong Yuan
中科院分区：
文献类型：
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作者：
Junyi Liu;E. Naidu;Jialian Wu;Shira Gabriel;E. Steinfeld;Junsong Yuan
环境传感技术的最新进展更多地侧重于测量环境的物理特性，例如温度和噪声，但缺乏理解主观反应或对环境的感受的能力，例如室内舒适度。感觉取决于环境条件以及个人需求和偏好。不同的人在同一个房间里，面对同样的条件，可能会有不同的感觉。在这项工作中，我们应用基于群体感知的方法来预测个性化的室内舒适度。我们假设相似的用户对舒适度有相似的感受，并且室内舒适度与一组固定的条件有关，例如空间、湿度、温度。我们调查了案例研究大楼的现有用户，并利用他们的回答来学习如何预测新用户的个人反应。从技术上讲，我们应用图卷积矩阵完成（GC-MC）方法来预测其他用户的舒适度，通过学习用户配置文件及其评分之间对一组固定调查问题的依赖关系。我们收集了一个厨房调查数据集，包含 59 个问题和总共 29 个不同背景的用户。
Recent progress in environment sensing technology focuses more on measuring the physical properties of the environment, e.g., temperature and noise, but lacks the ability to understand subjective responses, or feelings about the environment, e.g., indoor comfort. Feelings depend on both environmental conditions and individual needs and preferences. Different people may feel differently in the same room experiencing the same conditions. In this work, we apply a crowdsensing based approach to predict personalized indoor comfort. We assume that similar users share similar feelings about comfort, and that indoor comfort is related to a fixed set of conditions, e.g., space, humidity, temperature. We surveyed existing users of a case study building and used their responses to learn how to predict the personal responses of new users. Technically, we apply a graph convolutional matrix completion (GC-MC) method to predict the comfort of other users, by learning the dependency between the user profiles and their ratings to a fixed set of survey questions. We collect a kitchen survey dataset of 59 questions and in total 29 users of diverse profiles.