Recommender systems are essential tools that assist users in navigating vast information spaces by filtering and ranking options based on their preferences and behaviors. While traditional recommender systems focus on individual users, group recommender systems extend this concept to scenarios where items are consumed collectively. These systems must aggregate diverse user preferences and resolve conflicts to generate recommendations that satisfy the group as a whole. Various aggregation techniques, including social choice-based methods, are employed to achieve this goal. Additionally, contextual factors, such as group size and decision-making styles, play a relevant role in shaping recommendations. Beyond enhancing accuracy, trustworthiness has become an important concern in recommender systems, aligning with European AI guidelines that emphasize transparency, accountability, and fairness. Explainability is central to fostering trust, enabling users to understand the rationale behind recommendations. This chapter explores post-hoc explainability approaches, which offer flexible explanations that can be integrated into various recommendation frameworks. Specifically, the chapter explores the use of SHapley Additive exPlanations for recommendation explanations. Despite some research efforts in this direction, a systematic analysis is lacking. This contribution aims to bridge that gap by examining SHAP’s applicability in recommender systems, offering through a PRISMA-based systematic review, insights into its potential for enhancing transparency and user trust. A general framework for explaining individual and group recommendation using Shapley Additive Explanations is provided. Furthermore, a case study is developed to show how the proposal can effectively accomplish its goal.