Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

The ability to control for the kinds of information encoded in neural representation has a
variety of use cases, especially in light of the challenge of interpreting these models. We …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …

Fairness-aware recommendation alleviates discrimination issues to build trustworthy
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …

In this paper, we propose a new privacy solution for the data used to train a recommender
system, ie, the user–item matrix. The user–item matrix contains implicit information, which …

Trained classification models can unintentionally lead to biased representations and
predictions, which can reinforce societal preconceptions and stereotypes. Existing debiasing …

Fairness-aware recommendation eliminates discrimination issues to build trustworthy
recommendation systems. Existing fairness-aware approaches ignore accounting for rich …