In group recommendation systems, achieving a balance between fairness and diversity is a challenging yet crucial task, particularly in sequential settings where preferences evolve over multiple iterations. This paper introduces ADAPT, a novel framework designed to optimize fairness and diversity in sequential group recommendations. ADAPT employs two novel aggregation methods, FaDJO and DiGSFO, to equitably meet group members’ needs while promoting diverse content. In addition to the novel aggregation methods ADAPT introduces a novel definition for the inter-round diversity based on item-lists embeddings. Experimental results on two real datasets and different group formation demonstrate ADAPT’s ability to optimize user satisfaction, fairness, and diversity, outperforming baseline methods in two different metrics (f-score and NDCG) and highlighting the importance of balancing these critical factors in sequential group settings.

The city, as a hub of human activity, represents a crucial investigation field for data-driven decision making. The main goal of my PhD project is to formalize a methodology to guide and describe the entire analysis of the built environment from a sustainability perspective, achieving a better integration of the available data with respect to already existing smart city models. Using an interdisciplinary approach, we aim to: automatize data collection and cleaning; create a conceptual model to represent data characterizing the city structure and performance; use this model to enable integration and analysis of the collected data using data exploration and mining techniques.