Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Recently, graph neural network (GNN) has become the new state-of-the-art approach in
many recommendation problems, with its strong ability to handle structured data and to …

Graph Neural Network (GNN) based recommender systems have been attracting more and
more attention in recent years due to their excellent performance in accuracy. Representing …

Offline reinforcement learning (RL), a technology that offline learns a policy from logged data
without the need to interact with online environments, has become a favorable choice in …

By providing personalized suggestions to users, recommender systems have become
essential to numerous online platforms. Collaborative filtering, particularly graph-based …

Feed recommendation systems, which recommend a sequence of items for users to browse
and interact with, have gained significant popularity in practical applications. In feed …

Recommender systems usually face the issue of filter bubbles: over-recommending
homogeneous items based on user features and historical interactions. Filter bubbles will …

Neural graph collaborative filtering has received great recent attention due to its power of
encoding the high-order neighborhood via the backbone graph neural networks. However …

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 …

Existing research efforts for multi-interest candidate matching in recommender systems
mainly focus on improving model architecture or incorporating additional information …