As its availability and generality in online services, implicit feedback is more commonly used
in recommender systems. However, implicit feedback usually presents noisy samples in real …

Collaborative Filtering~(CF) typically suffers from the significant challenge of popularity bias
due to the uneven distribution of items in real-world datasets. This bias leads to a significant …

With the applications of recommendation systems rapidly expanding, an increasing number
of studies have focused on every aspect of recommender systems with different data inputs …

With the surge in mobile gaming, accurately predicting user spending on newly downloaded
games has become paramount for maximizing revenue. However, the inherently …

Conversion rate (CVR) prediction is essential in recommender systems, facilitating precise
matching between recommended items and users' preferences. However, the sample …

Estimating the post-click conversion rate (CVR) accurately in ranking systems is crucial in
industrial applications. However, this task is often challenged by data sparsity and selection …

Multimedia recommendation, which incorporates various modalities (eg, images, texts, etc.)
into user or item representation to improve recommendation quality, has received …

In the realm of online digital advertising, conversion rate (CVR) prediction plays a pivotal
role in maximizing revenue under cost-per-conversion (CPA) models, where advertisers are …

In the realm of online advertising, optimizing conversions is crucial for delivering relevant
products to users and enhancing business outcomes. Predicting conversion events is …

Learning user preferences from implicit feedback is one of the core challenges in
recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore …