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

TL;DR

This paper reviews how recommender systems and reinforcement learning are used in indoor environments to improve occupant well-being and energy efficiency, highlighting current applications and future research opportunities.

Contribution

It employs text mining and NLP to analyze a large corpus of literature, revealing trends and potential for expanding AI-driven indoor environment management.

Findings

Extensive use of recommendation systems and RL in space optimization and personalized control.

Identification of new application areas like predictive maintenance and environment customization.

Potential for integrating advanced language models to enhance understanding of academic literature.

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. 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.