Calibration in recommender systems ensures that the user's interests distribution over
groups of items is reflected with their corresponding proportions in the recommendation …

In Sequential Recommendation Systems, Cross-Entropy (CE) loss is commonly used but
fails to harness item confidence scores during training. Recognizing the critical role of …

In recent years, there has been an increasing recognition that when machine learning (ML)
algorithms are used to automate decisions, they may mistreat individuals or groups, with …

Recommender systems, while transformative in online user experiences, have raised
concerns over potential provider-side fairness issues. These systems may inadvertently …

Recommender systems usually aim to optimize accuracy in a supervised setting. Due to
various data biases, they often fail to enhance other critical qualities that go beyond …

Calibration in recommender systems is an important performance criterion that ensures
consistency between the distribution of user preference categories and that of …

Recommender Systems use the user's profile to generate a recommendation list with
unknown items to a target user. Although the primary goal of traditional recommendation …

Well-calibrated predictions of user preferences are essential for many applications. Since
recommender systems typically select the top-N items for users, calibration for those top-N …

Plot text is very valuable supporting information in movie recommendations. It has several
characteristics: 1) It is rich in content. Each movie often has a document of more than 200 …

In many recommender systems and search problems, presenting a well balanced set of
results can be an important goal in addition to serving highly relevant content. For example …