Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. A large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our AI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micromoments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. A supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine.

Among the large number of efforts that study the role of technology in energy saving, there exists, first, frameworks for monitoring and controlling energy consumption in households, second, systems that suggest best practices to users and energy providers, and third, findings that focus on the motivations that trigger a behavioral change toward energy efficiency. However, there is still no work that builds on the habitual behavior change of individuals and the use of technology for this purpose. In this paper, we survey the literature that aims at understanding the behavior of energy consumers and then changing it by recommending energy-saving activities. We build on the concept of “micro-moments” and the “habit loop” theory to explain how we can gradually change user routines by targeting those specific moments where the proper recommendation and a small but instant reward can be more effective in shaping a better energy-efficient profile.

Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data sparsity problem. This survey paper presents an in-depth analysis of the paradigm shift from conventional recommender systems to generative pre-trained-transformers-(GPT)-based chatbots. We highlight recent developments that leverage the power of GPT to create interactive and personalized conversational agents. By exploring natural language processing (NLP) and deep learning techniques, we investigate how GPT models can better understand user preferences and provide context-aware recommendations. The paper further evaluates the advantages and limitations of GPT-based recommender systems, comparing their performance with traditional methods. Additionally, we discuss potential future directions, including the role of reinforcement learning in refining the personalization aspect of these systems.

In the age of information overload, recommender systems have emerged as essential tools, assisting users in decision-making processes by offering personalized suggestions. However, their effectiveness is contingent on the availability of large amounts of user data, raising significant privacy and security concerns. This review article presents an extended analysis of recommender systems, elucidating their importance and the growing apprehensions regarding privacy and data security. Federated Learning (FL), a privacy-preserving machine learning approach, is introduced as a potential solution to these challenges. Consequently, the potential benefits and implications of integrating FL with recommender systems are explored and an overview of FL, its types, and key components, are provided. Further, the privacy-preserving techniques inherent to FL are discussed, demonstrating how they contribute to secure recommender systems. By illustrating case studies and significant research contributions, the article showcases the practical feasibility and benefits of combining FL with recommender systems. Despite the promising benefits, challenges, and limitations exist in the practical deployment of FL in recommender systems. This review outlines these hurdles, bringing to light the security considerations crucial in this context and offering a balanced perspective. In conclusion, the article signifies the potential of FL in transforming recommender systems, paving the path for future research directions in this intersection of technologies.