Under presentation bias, the attention of the users to the items in a recommendation list changes, thus affecting their possibility to be considered and the effectiveness of a model. When comparing different layouts through which recommendations are presented, presentation bias impacts users clicking behavior (low-level feedback), but not so much the perceived performance of a …
Day: August 3, 2023
FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models
Modern user profiling approaches capture different forms of interactions with the data, from user-item to user-user relationships. Graph Neural Networks (GNNs) have become a natural way to model these behaviors and build efficient and effective user profiles. However, each GNN-based user profiling approach has its own way of processing information, thus creating heterogeneity that does …
Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives
This tutorial was presented at the UMAP ‘23 conference, with Erasmo Purificato and Ernesto William De Luca. On the tutorial’s website, you can find the slides and the notebooks of the hands-on parts. The proposed tutorial aimed to introduce the UMAP community to modern user profiling approaches leveraging graph neural networks (GNNs). We will begin …
Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation
Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths between recommended products and already experienced products and, then, turn such paths into textual explanations for the user. A benchmarking of the state-of-the-art approaches, in terms of accuracy and beyond-accuracy perspectives, …
Practical perspectives of consumer fairness in recommendation
The mitigation of consumer fairness assumes that recommendations bring equitable effectiveness for the different demographic groups of users. Mitigation approaches can be analyzed from, multiple, technical perspectives. Different mitigation strategies at the state of the art offer different properties. In a study, published by the Information Processing and Management journal (Elsevier) and conducted with Gianni …
Bias characterization, assessment, and mitigation in location-based recommender systems
Location-based recommender systems (LBRSs) provide suggestion for Points of Interest (POIs) in Location-based social networks. However, we can characterize different forms of bias, associated with polarized interactions of the users with the PoIs. Post-processing and hybrid mitigation approaches can help alleviate the impact of those biases. In a study, published in the Data Mining and …