Recent years have witnessed the great accuracy performance of graph-based Collaborative
Filtering (CF) models for recommender systems. By taking the user-item interaction behavior …

Collaborative Filtering (CF) by learning from the wisdom of crowds has become one of the
most important approaches to recommender systems research, and various CF models have …

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings
based on the user-item bipartite graph structure, and have achieved state-of-the-art …

The graph neural network-based collaborative filtering (CF) models user-item interactions as
a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately …

The recommender system is a popular research topic in the past decades, and various
models have been proposed. Among them, collaborative filtering (CF) is one of the most …

User and item attributes are essential side-information; their interactions (ie, their co-
occurrence in the sample data) can significantly enhance prediction accuracy in various …

Item-based collaborative filtering (ICF) has been widely used in industrial applications due
to its good interpretability and flexible composability. The main idea of ICF is to characterize …

Collaborative filtering (CF) is one of the most popular and important recommendation
methodologies in the heart of numerous recommender systems today. Although widely …

In recent years, Graph Neural Networks (GNNs) have been widely used in Collaborative
Filtering (CF), one of the most popular methods in recommender systems. However, most …

Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The
learning process of CF models typically consists of three components: interaction encoder …