As user preferences rapidly and continually evolve, it becomes crucial to incorporate these temporal dynamics in the design of recommender systems. This paper proposes a novel dynamic and overlapping community-based recommender system framework, which considers the evolving nature of users’ preferences to suggest items distinctly for both individuals and groups. Using temporal networks to capture the dynamic interplay of users’ interests, we apply dynamic community detection techniques to identify similar items, thereby addressing scalability issues. To cater to group recommendation scenarios, the framework leverages users’ satisfaction with similar items, thereby mitigating data sparsity and delivering a broad range of recommendations. Experimental results show that the proposed system outperforms state-of-the-art approaches in terms of recommendation accuracy for both individual and group recommendations.