The scarcity of realistic datasets poses a significant challenge in benchmarking recommender systems and social network analysis methods and techniques. A common and effective solution is to generate synthetic data that simulates realistic interactions. However, although various methods have been proposed, the existing literature still lacks generators that are fully adaptable and allow easy manipulation of the underlying data distributions and structural properties. To address this issue, the present work introduces GenRec, a novel framework for generating synthetic user-item interactions that exhibit realistic and well-known properties observed in recommendation scenarios. The framework is based on a stochastic generative process based on latent factor modeling. Here, the latent factors can be exploited to yield long-tailed preference distributions, and at the same time they characterize subpopulations of users and topic-based item clusters. Notably, the proposed framework is highly flexible and offers a wide range of hyper-parameters for customizing the generation of user-item interactions. The code used to perform the experiments is publicly available at https://anonymous.4open.science/r/GenRec-DED3.
