The concept of sustainability has become a central focus across multiple sectors, driven by the urgent need to address climate change and protect the environment. Technological advancements and capabilities, together with the emergence of new ecological issues, are leading to growing awareness and influencing shifts in multiple areas such as energy, transportation, and waste management. Within this context, the roles of recommender systems represent a promising solution, since people need guidance and occasionally a gentle push to translate their intentions into actions or to bring goals to life. However, existing literature reveals a fragmented landscape, with solutions often addressing specific aspects or recommendation contribution in isolation. Many sustainability interventions focus solely on providing consumption data and environmental insights, while others emphasize learning and behavior change strategies. My doctoral project aims to address this gap by leveraging various approaches to recommender systems and applying them in sustainability contexts, with the goal to build a holistic system that maximizes the contributions of these diverse methods, also integrating user-centric and value-driven perspectives. This research project delves into two distinct facets: energy sustainability and sustainable mobility. The first case centers on enhancing energy efficiency within energy communities through personalized recommendations and engagement strategies. The second facet focuses on reshaping user commuting patterns towards sustainable alternatives, by recommending suitable and more sustainable modes of transportation, such as cycling, carpooling, and public transportation. Both cases share the same objective: align user behaviors with sustainability goals, thereby reducing individual environmental impact and enhancing the sense of belonging to a community, whether this is confined to a group of individuals or pertains to society at large. An innovative comprehensive recommendation system approach is highly beneficial since it can take advantage of all the existing contributions combined in a framework that makes at the same time different types of recommendations: explainable, educative, behavioral and social-aware, addressing the complexities of this multifaceted domain.