The sequence of user-item interactions can be effectively incorporated in the generation of personalized explanations in recommender systems. By modeling user behavior history sequentially, it is possible to enhance the quality and personalization of explanations provided alongside recommendations, without affecting recommendation quality. In a study with Alejandro Ariza-Casabona, Maria Salamó, and Gianni Fenu, published in …
Looks Can Be Deceiving: Linking User-Item Interactions and User’s Propensity Towards Multi-Objective Recommendations
Users’ claimed willingness to interact with novel and diverse items doesn’t always match the recommendations they accept. While users may express a desire for novelty and diversity in recommendations, their actual choices often gravitate towards relevance. This key finding challenges the conventional approach in multi-objective recommender system design, emphasizing the necessity of aligning system objectives …
Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems
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 …
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 …
Reinforcement recommendation reasoning through knowledge graphs for explanation path quality
Knowledge Graph-based recommender systems naturally produce explainable recommendations, by showing the reasoning paths in the knowledge graph (KG) that were followed to select the recommended items. One can define metrics that assess the quality of the explanation paths in terms of recency, popularity, and diversity. Combining in- and post-processing approaches to optimize for both recommendation …
Enabling Reproducibility in Group Recommender Systems
Group recommender systems produce suggestions in contexts in which more than one person is involved in the recommendation process. They present additional tasks w.r.t. those for single users, such as the identification of the groups, or their modeling. While this clearly amplifies the possible reproducibility issues, to date, no framework to benchmark group recommender systems …