<p>One of the biggest challenges in contemporary recommendation systems is finding an adequate balance between user preferences and group consensus. This paper presents a novel Dynamic Preference Adjustment Concession Strategy (DPACS) approach to tackle this problem in the context of POI recommendations for a group of users. Our approach improves the quality and relevance of recommendations by dynamically modifying user preferences in response to user feedback and interactions. It integrates an adaptively evolving compromise factor (CF) to balance the group’s goals with individual users’ preferences. The main elements of our approach are the extended Pareto frontier, an agreement threshold to ensure fairness, and a dynamic adjustment mechanism for CF. Combined, these components enable a more responsive and versatile recommendation system that can adjust to different levels of user consent. The efficacy of our approach is validated through experiments using real-world datasets, Gowalla, Foursquare, and Swarm. Results demonstrate significant improvements in recommendation quality, measured by various metrics, including Group Satisfaction (GS), fairness, Pareto Efficiency, precision, and nDCG.</p>