The goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are
positively-related but have not been interacted yet, where only a small portion of positive …

Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the
noise issues from data cleaning perspective such as data resampling and reweighting, but …

Graph Neural Network (GNN) based social recommendation models improve the prediction
accuracy of user preference by leveraging GNN in exploiting preference similarity contained …

The ubiquity of implicit feedback makes them the default choice to build modern
recommender systems. Generally speaking, observed interactions are considered as …

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 …

In the era of Internet of Things (IoT), intelligent recommendation is playing an important role
in our daily life. How to provide personalized information to users is the core concern of …

Historical user-item interaction datasets are essential in training modern recommender
systems for predicting user preferences. However, the arbitrary user behaviors in most …

Graph-based recommendation systems are blossoming recently, which models user-item
interactions as a user-item graph and utilizes graph neural networks (GNNs) to learn the …

In recommender systems, click behaviors play a fundamental role in mining users' interests
and training models (clicked items as positive samples). Such signals are implicit feedback …

Learning vector representations (aka, embeddings) of users and items lies at the heart of
building a modern recommender system. Typically, graph neural networks (GNN) have been …