mlsanityData to 14 Oct 2026

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

arXiv 2411.08734 ↗First posted 13 Nov 2024eess.SY cs.LG cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

Builds on

12 of 145 references.

References · 12

  1. 2017Mask R-CNNcited by 7,277
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  3. 2015Fast R-CNNcited by 2,933
  4. 2021Reinforcement learning based recommender systems: A surveycited by 188
  5. 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 127
  6. 2020A Review of Deep Reinforcement Learning for Smart Building Energy Managementcited by 38
  7. 2019Hybrid Recommender Systems: A Systematic Literature Reviewcited by 16
  8. 2021A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospectscited by 13
  9. 2020The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiencycited by 9
  10. 2022Utility-Based Context-Aware Multi-Agent Recommendation System for Energy Efficiency in Residential Buildingscited by 3
  11. 2022Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less datacited by 2
  12. 2022Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collectioncited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026BuildOcc: A Large Language Model Occupant Agent Platform for Building Energy Research
  2. 2026Personalized and Trust-Aware Health Recommendation Policies for a Construction Workplace ↗
  3. 2025Conversational Agents for Building Energy Efficiency -- Advising Housing Cooperatives in Stockholm on Reducing Energy Consumption
  4. 2025Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control
  5. 2025BuildingGym: An open-source toolbox for AI-based building energy management using reinforcement learning
  6. 2026OccuReward: LLM-Guided Occupant-Centric Reward Shaping for Demographic Equity in Grid-Interactive Buildings
  7. 2026Retrieval over Reasoning: A Cost-Controlled Benchmark of Language Models for Energy-Retrofit Recommendation ↗
  8. 2026From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing