Path-based Reasoning on Knowledge Graphs for Explainable Recommender Systems

Recommender systems are central in modern knowledge-intensive applications. Beyond predicting user-item interactions, this class of systems increasingly requires models to reason over heterogeneous entities, relations, and contextual evidence while remaining transparent, trustworthy, and reusable. Path reasoning over knowledge graphs provides a principled foundation for this goal, as it connects users, items, and knowledge through semantically meaningful chains that support explainable recommendation. However, adoption remains limited by fragmented implementations, heterogeneous preprocessing pipelines, and high coding complexity. This tutorial introduces hopwise, a framework for building explainable-by-design recommender systems through path reasoning on knowledge graphs. Participants follow a progressive hands-on workflow: transforming recommendation datasets into knowledge graphs, running reinforcement learning-based and language model-based reasoning methods, evaluating their utility and explainability, and extending the framework from multiple perspectives.

Ludovico Boratto webpage is Associate Professor at the Dept. of Mathematics and Computer Science of the University of Cagliari (Italy). His research interests focus on recommender systems and their impact on stakeholders, with over 130 papers published in top-tier conference proceedings and journals. He has delivered tutorials and invited talks at major venues, including CIKM, UMAP, RecSys, ICDE, ECIR, WSDM, ICDM, DSAA, and ECAI. He is an editorial board member of Information Processing & Management (Elsevier) and Journal of Intelligent Information Systems (Springer). He regularly serves on the program committees of leading conferences, where he has received four outstanding contribution awards. In this tutorial, he provides the core conceptual bridge, enabling participants to quickly build a solid foundation in responsible recommendation within the broader CIKM context.

Gianni Fenu webpage is Full Professor at the Dept. of Mathematics and Computer Science of the University of Cagliari (Italy). His research interests focus on responsible recommender systems, digital education, and personalization. He has authored more than 150 papers in conferences and journals, and has led several national and European projects, including ILEARNTV MIUR-UE (2014-2017, 10 M€, 6 partners) and the European Research M-Commerce and Development project. In this tutorial, he contributes the application- and impact-oriented perspective, facilitating the connection to knowledge-intensive platforms.

Mirko Marras webpage is Tenure-Track Assistant Professor at the Dept. of Mathematics and Computer Science of the University of Cagliari (Italy). His research ranges across various domains impacted by user modeling and personalization. He has co-authored more than 120 papers in top-tier conferences and journals, and given tutorials at ECML-PKDD, RecSys, ICDE, ECIR, WSDM, ICDM, and UMAP. He is part of the program committees of top-tier conferences, where he received four outstanding reviewer awards. He is an associate editor for Springer's Journal of Ambient Intelligence and Humanized Computing and Neural Processing Letters. In this tutorial, he is responsible for introducing the main operative parts conceptually (knowledge graphs, path reasoning algorithms and evaluation metrics).

Francesca Maridina Malloci webpage is Non-Tenure Track Assistant Professor at the Dept. of Mathematics and Computer Science of the University of Cagliari (Italy). Her research focuses on predictive analytics and decision-making algorithms in multi-stakeholder contexts, with particular attention to recommender systems. She has published extensively in international journals and conference proceedings. She has delivered tutorials at ECML-PKDD and co-organized workshops on related themes at ECML-PKDD, UMAP, and SIGIR. She has also delivered course-level tutorials for doctoral students on hopwise, leading the preparation of the teaching materials that form the basis of the present tutorial (most recently at Boise University, July 2025). In 2019, she was a visiting scientist at the Eurecat (Spain), collaborating with the Data Science and Big Data Analytics Unit.

Giacomo Medda webpage is Postdoctoral Researcher at the Dept. of Mathematics and Computer Science of the University of Cagliari. His research spans recommender systems, responsible artificial intelligence, graph modeling, and generative artificial intelligence. He has authored over 25 publications in leading conferences and journals. He co-edited the special issue “Knowledge Discovery from Graphs” in Springer's Data Mining and Knowledge Discovery journal and regularly serves on the program committees of UMAP, RecSys, and SIGIR. He co-organized the IRonGraphs workshop at ECIR 2024. He is a core architect and lead developer of hopwise, and directly supports participants in understanding and extending its internal components.

Alessandro Soccol webpage is a Research Associate at the Dept. of Mathematics and Computer Science of the University of Cagliari (Italy). His research focuses on explainable recommender systems, personalization, and uncertainty quantification. He has co-authored papers published in top-tier venues, such as CIKM and RecSys, and serves on the RecSys Program Committee. He is a core developer of hopwise and is responsible for key path reasoning and explainability modules, which he actively demonstrates and helps participants play with.