Collaborative filtering (CF) models easily suffer from popularity bias, which makes
recommendation deviate from users' actual preferences. However, most current debiasing …

Although recommender systems (RSs) play a crucial role in our society, previous studies
have revealed that the performance of RSs may considerably differ between groups of …

Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

Collaborative Filtering (CF) models, despite their great success, suffer from severe
performance drops due to popularity distribution shifts, where these changes are ubiquitous …

In recommender systems, top-N recommendation is an important task with implicit feedback
data. Although the recent success of deep learning largely pushes forward the research on …

Offline evaluation is an essential complement to online experiments in the selection,
improvement, tuning, and deployment of recommender systems. Offline methodologies for …

Research on recommendation systems is swiftly producing an abundance of novel methods,
constantly challenging the current state-of-the-art. Inspired by advancements in many …

Collaborative filtering algorithms capture underlying consumption patterns, including the
ones specific to particular demographics or protected information of users, eg, gender, race …

Recommender models are hard to evaluate, particularly under offline setting. In this article,
we provide a comprehensive and critical analysis of the data leakage issue in recommender …

Offline evaluation is a popular approach to determine the best algorithm in terms of the
chosen quality metric. However, if the chosen metric calculates something unexpected, this …