Over the past decades, for One-Class Collaborative Filtering (OCCF), many learning
objectives have been researched based on a variety of underlying probabilistic models …

The majority of recent work in latent Collaborative Filtering (CF) has focused on developing
new model architectures to learn accurate user and item representations. Typically, a …

Recommender systems play an important role in helping users discover items of interest
from a large resource collection in various online services. Although current deep neural …

Recommender systems play an important role in providing personalized services for users
in the context of information overload. Generally, users' feedback toward items often contain …

Heterogeneous one-class collaborative filtering is an emerging and important problem in
recommender systems, where two different types of one-class feedback, ie, purchases and …

While effective in recommendation tasks, collaborative filtering (CF) techniques face the
challenge of data sparsity. Researchers have begun leveraging contrastive learning to …

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally
minimizing the empirical risks averaged over all the observed data. However, the global …

Recommendation systems aim to analyze users' historical behaviors to recommend items
that suit their preferences. In the real world, users' feedback is usually heterogeneous, such …

Collaborative filtering is a fundamental building block in many recommender systems. While
most of the existing collaborative filtering methods focus on explicit, multi-class settings (eg …

Abstract Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data
sparsity and cold-start problems present in recommendation systems by exploiting the …