BeHAVE: Synthetic Data Generator for Group Recommender Systems

Several distinct strategies have been proposed for recommending relevant items to groups of users. However, comparing the performance of these systems remains challenging due to the lack of representative datasets that capture the diversity of scenarios where group recommendation is needed, including variations in group composition and user behavior. To address this limitation and enable in-depth benchmarking, this article makes two main contributions. The first is an empirical analysis of widely used real-world GRS datasets, based on an adaptabilitydriven characterization of user behavior when interacting with items in group settings. This analysis compares users' individual preferences with those expressed during group interactions, while also examining the diversity of items involved. Building on these findings, we introduce BeHAVE, the first configurable dataset generator specifically designed for group recommendation. On top of a domain knowledge graph, BeHAVE allows controlled simulation of group interactions through behavioral parameters such as user adaptability and item diversity. Experimental results demonstrate that BeHAVE can replicate a range of behavioral patterns observed in real-world datasets and serves as a flexible benchmarking tool for evaluating GRS models under varying group dynamics.