Diego Matteo Antognini

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Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences. We expand the variational autoencoder collaborative filtering from a single-domain to a multi-domain setting. T ...

Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we are working towards fully automating the visual inspections to support the pr ...

Automated predictions require explanations to be interpretable by humans. Past work used attention and rationale mechanisms to find words that predict the target variable of a document. Often though, they result in a tradeoff between noisy explanations or ...

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 approach to designing fair algorithms is to use relaxations of fairness not ...

Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one han ...

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 costly, existing datasets contain only hundreds of samples at most, resulting in ...

There has recently been growing interest in the automatic generation of cooking recipes that satisfy some form of dietary restrictions, thanks in part to the availability of online recipe data. Prior studies have used pre-trained language models, or relied ...

Term extraction is an information extraction task at the root of knowledge discovery platforms. Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains r ...

Explanation is important for text classification tasks. One prevalent type of explanation is rationales, which are text snippets of input text that suffice to yield the prediction and are meaningful to humans. A lot of research on rationalization has been ...

Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow users to provide feedback by critiquing them. Several algorithms for recomme ...

Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender systems with both correlated and conflicting objectives. Classic multi-grad ...

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 perspective of multimodal generative models, recent work has proposed M&Ms-VAE, w ...

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting ...

Simulating online recommender system performance is no- toriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evalu- ate model performan ...

Automated predictions require explanations to be interpretable by humans. One type of ex- planation is a rationale, i.e., a selection of in- put features such as relevant text snippets from which the model computes the outcome. How- ever, a single overall ...

Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on h ...

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 require a large number of data to achieve their best performance. Many datasets fu ...

Diego Matteo Antognini

Chattez avec Graph Search

Textual Explanations and Critiques in Recommendation Systems

Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts

Active Learning for Imbalanced Civil Infrastructure Data

Multi-Dimensional Explanation of Target Variables from Documents

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Fast Multi-Step Critiquing for VAE-based Recommender Systems

GameWikiSum: a Novel Large Multi-Document Summarization Dataset

Assistive Recipe Editing through Critiquing

Unsupervised Term Extraction for Highly Technical Domains

Interlock-Free Multi-Aspect Rationalization for Text Classification

Multi-Step Critiquing User Interface for Recommender Systems

Momentum-based Gradient Methods in Multi-Objective Recommendation

Positive and Negative Critiquing for VAE-based Recommenders

Interacting with Explanations through Critiquing

Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context

Rationalization through Concepts

Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization

HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset