  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.
