It is possible to effectively address consumer unfairness in recommender systems by using counterfactual explanations to augment the user-item interaction graph. This approach not only leads to fairer outcomes across different demographic groups but also maintains or improves the overall utility of the recommendations. In a study with Francesco Fabbri, Gianni Fenu, Mirko Marras, and …
Day: November 18, 2023
Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives
Controlling various objectives within Multi-Objective Recommender Systems (MORSs). While reinforcing accuracy objectives appears feasible, it is more challenging to individually control diversity and novelty due to their positive correlation. This raises critical questions about the effectiveness of incorporating multiple correlated objectives in MORSs and the potential risks of not having control over them. In a …
Towards Self-Explaining Sequence-Aware Recommendation
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 …