23 citations · 56 across the 18 of their papers we have counts for
7 papers · 1 filter
An Enhanced MeanSum Method For Generating Hotel Multi-Review Summarizations
Saibo Geng, Diego Antognini
Multi-document summaritazion is the process of taking multiple texts as input and producing a short summary text based on the content of input texts. Up until recently, multi-docum…
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…
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
Kirtan Padh, Diego Antognini, Emma Lejal Glaude +2
The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One…
Interacting with Explanations through Critiquing
Diego Antognini, Claudiu Musat, Boi Faltings
Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs t…
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…
GameWikiSum: a Novel Large Multi-Document Summarization Dataset
Diego Antognini, Boi Faltings
Today's research progress in the field of multi-document summarization is obstructed by the small number of available datasets. Since the acquisition of reference summaries is cost…
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…