205 citations · 861 across the 59 of their papers we have counts for
8 papers · 1 filter
Positive and Negative Critiquing for VAE-based Recommenders
Diego Antognini, Boi Faltings
Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the persp…
Multi-Step Critiquing User Interface for Recommender Systems
Diana Petrescu, Diego Antognini, Boi Faltings
Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow…
Fast Multi-Step Critiquing for VAE-based Recommender Systems
Diego Antognini, Boi Faltings
Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to re…
Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context
Milena Filipovic, Blagoj Mitrevski, Diego Antognini +3
Recommender systems research tends to evaluate model performance offline and on randomly sampled targets, yet the same systems are later used to predict user behavior sequentially…
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset
Diego Antognini, Boi Faltings
Today, recommender systems are an inevitable part of everyone's daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models requir…
Multi-Gradient Descent for Multi-Objective Recommender Systems
Nikola Milojkovic, Diego Antognini, Giancarlo Bergamin +2
Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender sy…