Alessandro De Bellis, Salvatore Bufi, Giovanni Servedio, Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Recommender systems have demonstrated a significant impact across diverse domains, yet ensuring the reproducibility of experimental findings remains a persistent challenge.A primary obstacle lies in the fragmented and often opaque data management strategies employed during the preprocessing stage, where decisions about dataset selection, filtering, and splitting can substantially influence outcomes.To address these limitations, we introduce DataRec, an open-source Python-based library specifically designed to unify and streamline data handling in recommender system research.By providing reproducible routines for dataset preparation, data versioning, and seamless integration with other frameworks, DataRec promotes methodological standardization, interoperability, and comparability across different experimental setups.Our design is informed by an in-depth review of 55 stateof-the-art recommendation studies, ensuring that DataRec adopts best practices while addressing common pitfalls in data management.Ultimately, our contribution facilitates fair benchmarking, enhances reproducibility, and fosters greater trust in experimental results within the broader recommender systems community.The DataRec library, documentation, and examples are freely available at https://github.com/sisinflab/DataRec.

Data minimization, required by recent data privacy regulations, is crucial for user privacy, but its impact on recommender systems remains largely unclear.The core problem lies in the fact that reducing or altering the training data of these systems can drastically affect their performance.While previous research has explored how data minimization affects recommendation accuracy, a critical gap remains: How does data minimization impact consumers' and providers' fairness?This study addresses this gap by systematically examining how data minimization influences multiple objectives in recommender systems, i.e., the trade-offs between accuracy, user fairness, and provider fairness.Our investigation includes (i) an analysis of how the data minimization strategies affect RS performance across these objectives, (ii) an assessment of data minimization techniques to determine those that can balance better the trade-off among the considered objectives, and (iii) an evaluation of the robustness of different recommendation models under diverse minimization strategies to identify those that best maintain performance.The findings reveal that data minimization can sometimes undermine provider fairness, albeit enhancing groupbased consumer fairness to the detriment of accuracy.Additionally, different strategies can offer diverse trade-offs for the assessed objectives.The source code supporting this study is available at https://github.com/salvatore-bufi/DataMinimizationFairness.

Knowledge graphs (KG) have been proven to be a powerful source of side information to enhance the performance of recommendation algorithms. Their graph-based structure paves the way for the adoption of graph-aware learning models such as Graph Neural Networks (GNNs). In this respect, state-of-the-art models achieve good performance and interpretability via user-level combinations of intents leading users to their choices. Unfortunately, such results often come from and end-to-end learnings that considers a combination of the whole set of features contained in the KG without any analysis of the user decisions. In this paper, we introduce KGTORe, a GNN-based model that exploits KG to learn latent representations for the semantic features, and consequently, interpret the user decisions as a personal distillation of the item feature representations. Differently from previous models, KGTORe does not need to process the whole KG at training time but relies on a selection of the most discriminative features for the users, thus resulting in improved performance and personalization. Experimental results on three well-known datasets show that KGTORe achieves remarkable accuracy performance and several ablation studies demonstrate the effectiveness of its components. The implementation of KGTORe is available at: https://github.com/sisinflab/KGTORe.