In recent years, Recommendation Systems have become integral to the online experiences of consumers, particularly those that effectively integrate user interactions into their algorithms, enhancing both efficiency and adaptability. This article presents a comprehensive systematic review of the literature addressing classical problems in recommendation systems, specifically focusing on the consideration of user interaction. We employed a rigorous systematic literature review methodology, critically analyzing various proposals to identify their limitations, characteristics, and potential avenues for further research. Our investigation involved mapping relevant studies that examine how user interaction with recommendation systems is addressed and determining the extent to which this aspect has been explored. We established strict inclusion and exclusion criteria to select academic publications, resulting in a curated set of 29 scientific papers. The findings offer a snapshot of the primary characteristics of the identified works, revealing significant gaps that can inform future research directions. Our analysis indicates that most studies addressing user interaction emphasize preference elicitation and feedback mechanisms, predominantly focusing on improving the accuracy of recommendation rankings, with a notable concentration on the e-commerce domain.

Due to the environmental impacts of greenhouse gas emissions from traditional combustion vehicles, governments worldwide are encouraging the transition to electric vehicles (EVs). However, as EV use increases, user-related charging challenges have become evident. To identify possible solutions to improve EV charging management from a user-centered perspective, a state-of-the-art study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Optimization systems and artificial intelligence (AI) methods applied to decision-making were compared and a growing trend towards the implementation of artificial intelligence in current applications was identified. This study investigates in more depth the application of AI in multi-agent systems for energy management in EV charging. It provides a critical review of charging stations, focusing on aggregator-based models that operate within a multi-agent system in smart grids. This analysis adopts the vehicle owner’s perspective and considers the charging duration of the EV as a parameter. This article identifies significant gaps in how existing research addresses individual electric vehicle users, noting a lack of consideration for energy management and system connectivity to support EV recharging locations. This work presents solutions to these gaps by using aggregators and multi-agent systems to represent charging stations, facilitate user access, and improve energy management.