Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives


Erasmo Purificato, Otto von Guericke University Magdeburg, Germany and Leibniz Institute for Educational Media | Georg Eckert Institute, Germany

Ludovico Boratto, University of Cagliari, Italy

Ernesto William De Luca, Otto von Guericke University Magdeburg, Germany and Leibniz Institute for Educational Media | Georg Eckert Institute, Germany




This tutorial aims to introduce the UMAP community to modern user profiling approaches leveraging graph neural networks (GNNs). We will begin by discussing the conceptual foundations of user profiling and GNNs and providing a literature review of the two topics. We will then present a systematic overview of the state-of-the-art GNN architectures designed for user profiling, including the types of data that are typically used for this purpose. We will also discuss ethical considerations and beyond-accuracy perspectives (i.e. fairness and explainability), which can arise within the potential applications of adopting GNNs for user profiling. In the practical session of the tutorial, attendees will have the opportunity to understand concretely how recent GNN models for user profiling are built and trained with open-source tools and publicly available datasets. The audience will also be engaged in investigating the impact of the presented models on case studies involving bias detection and mitigation, as well as user profiles explanations. The tutorial will end with an analysis of existing and emerging open challenges in the field and their future research directions.

User Models as Digital Twins: Using Webassembly Techniques to ensure Privacy, Transparency and Control in Personalization User Models as Digital Twins Tutorial



Ralph Deters, University of Saskatchewan, Canada

Julita Vassileva, University of Saskatchewan, Canada


This half-day tutorial will show how web-assembly techniques can be used to create sandboxed user models as digital twins within web- and mobile applications. Such models can learn from the user’s behaviour and personalise the application to the user, while ensuring transparency of the model, and ability for the user to experiment and control the personalization. The advantage of using webassembly techniques to implement digital twin user models is that the user model is a plugin, insulated from the application, and thus the privacy of the user model can be ensured as well as the application’s compliance with the GDPR.

Tutorial on Accountable Knowledge-aware Recommender Systems


Pasquale Lops, University of Bari Aldo Moro, Italy

Cataldo Musto, University of Bari Aldo Moro, Italy

Marco Polignano, University of Bari Aldo Moro, Italy



Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems. The use of content of different types, such as text and images, to build a representation of users and items asks for new methods to extract descriptive features to adopt in the recommendation process. The literature on knowledge-aware recommender systems is actually rich and constantly evolving, in terms of both techniques and software libraries to implement them. This makes also difficult to define reproducible recommendation pipelines, making the accountability of recommender systems a challenge. Hence, this tutorial aims to present the most recent trends in the area of knowledge-aware recommender systems, including novel representation methods for text and images, and discuss how to implement reproducible pipelines for knowledge-aware recommender systems.

We will present a comprehensive Python framework, called ClayRS1 , to deal with knowledge-aware recommender systems aiming to provide:

  • a common ground for researchers and practitioners interested in the latest knowledge-aware techniques for user modeling and recommender systems
  • a practical way for implementing the whole recommendation pipeline, ranging from the content processing for text and images, to the generation of recommendations and the evaluation of their performance.