In the application recommendation field, collaborative filtering (CF) method is often
considered to be one of the most effective methods. As the basis of CF-based …

Collaborative filtering (CF) is dedicated to learning the representations of users and items
based on interactive data. Regrettably, the lack of fine-grained modeling of interactive …

Graph neural network-based recommender systems are blossoming recently, and it can
explicitly express user-item high-order connectivity information, so it can significantly …

The traditional collaborative filtering (CF) method based on static user preference modeling
and linear matching function learning severely limits the recommendation performance. To …

In recent years, neural networks have yielded immense success on speech recognition,
computer vision and natural language processing. However, the exploration of neural …

Extensive studies have fully proved the effectiveness of collaborative filtering (CF)
recommendation models based on graph convolutional networks (GCNs). As an advanced …

Strengthening the representation and learning of user vector and item vector is the key of
recommendation system. Neural Graph Collaborative Filtering (NGCF) has the problem of …

Collaborative Filtering (CF) is among the most successful techniques in recommendation
tasks. Recent works have shown a boost of performance of CF when introducing the …

Collaborative filtering (CF) is a widely used technique in recommender systems. While many
CF methods primarily focus on collaborative signals derived from user–item interactions …

Recently, Graph Convolutional Network (GCN) based methods have become novel state-of-
the-arts for Collaborative Filtering (CF) based Recommender Systems. To obtain users' …