Marco Túlio Ribeiro

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Performing accurate suggestions is an objective of paramount importance for effective recommender systems. Other important and increasingly evident objectives are novelty and diversity, which are achieved by recommender systems that are able to suggest diversified items not easily discovered by the users. Different recommendation algorithms have particular(More)
Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which(More)
Recommender systems are quickly becoming ubiquitous in applications such as e-commerce, social media channels, and content providers, among others, acting as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. A typical task in many recommender systems is to output a ranked list of(More)
Traditional content-based e-mail spam filtering takes into account content of e-mail messages and apply machine learning techniques to infer patterns that discriminate spams from hams. In particular, the use of content-based spam filtering unleashed an unending arms race between spammers and filter developers, given the spammers' ability to continuously(More)
Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of(More)
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