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

arXiv: https://arxiv.org/abs/2411.08734 · HTML: https://mlsanity.com/paper/2411.08734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 145 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 12 of 12)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [A Review of Deep Reinforcement Learning for Smart Building Energy Management](https://mlsanity.com/paper/2008.05074/) (2020)
- [Hybrid Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/1901.03888/) (2019)
- [A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects](https://mlsanity.com/paper/2102.07654/) (2021)
- [The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiency](https://mlsanity.com/paper/2010.04990/) (2020)
- [Utility-Based Context-Aware Multi-Agent Recommendation System for Energy Efficiency in Residential Buildings](https://mlsanity.com/paper/2205.02704/) (2022)
- [Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less data](https://mlsanity.com/paper/2208.03078/) (2022)
- [Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection](https://mlsanity.com/paper/2210.13977/) (2022)

## Abstract (arXiv)

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.