Beyond-Accuracy Perspectives on Graph Neural Network-Based Models for Behavioural User Profiling

The presented doctoral research aims to develop a behavioural user profiling framework focusing simultaneously on three beyond-accuracy perspectives: privacy, to study how to intervene on graph data structures of specific contexts and provide methods to make the data available in a meaningful manner without neither exposing personal user information nor corrupting the profiles creation and system performances; fairness, to provide user representations that are free of any inherited discrimination which could affect a downstream recommender by developing debiasing approaches to be applied on state-of-the-art GNN-based user profiling models; explainability, to produce understandable descriptions of the framework results, both for user profiles and recommendations, mainly in terms of interaction importance, by designing an adaptive and personalised user interface which provides tailored explanations to the end-users, depending on their specific user profiles.