Recommender systems and reinforcement learning for human-building interaction and context aware support: A text mining-driven review of scientific literature

This repository contains the data and code for paper:

Wenhao Zhang, Matias Quintana, Clayton Miller. Recommender systems and reinforcement learning for human-building interaction and context aware support: A text mining-driven review of scientific literature. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.115247.

@article{ZHANG2024115247,
title = {Recommender systems and reinforcement learning for human-building interaction and context aware support: A text mining-driven review of scientific literature},
author = {Wenhao Zhang and Matias Quintana and Clayton Miller},
journal = {Energy and Buildings},
pages = {115247},
year = {2024},
issn = {0378-7788},
doi = {https://doi.org/10.1016/j.enbuild.2024.115247}
}

Abstract

The indoor environment significantly impacts human health and well-being; enhancing health and reducing energy consumption in these settings is a central research focus. With the advancement of Information and Communication Technology (ICT), recommendation systems and reinforcement learning (RL) have emerged as promising approaches to induce behavioral changes to improve the indoor environment and energy efficiency of buildings. This study aims to employ text mining and Natural Language Processing (NLP) techniques to thoroughly examine the connections among these approaches in the context of human-building interaction and occupant context-aware support. The study analyzed 27,595 articles from the ScienceDirect database, revealing extensive use of recommendation systems and RL for space optimization, location recommendations, and personalized control suggestions. Although these systems are broadly applied to specific content, their use in optimizing indoor environments and energy efficiency remains limited. This gap likely arises from the need for interdisciplinary knowledge and extensive sensor data. Traditional recommendation algorithms, including collaborative filtering, content-based and knowledge-based methods, are commonly employed. However, the more complex challenges of optimizing indoor conditions and energy efficiency often depend on sophisticated machine learning (ML) techniques like reinforcement and deep learning. Furthermore, this review underscores the vast potential for expanding recommender systems and RL applications in buildings and indoor environments. Fields ripe for innovation include predictive maintenance, building-related product recommendation, and optimization of environments tailored for specific needs, such as sleep and productivity enhancements based on user feedback. The study also notes the limitations of the method in capturing subtle academic nuances. Future improvements could involve integrating and fine-tuning pre-trained language models to better interpret complex texts.

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