136 citations · 249 across the 7 of their papers we have counts for
6 papers · 1 filter
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation
Junwei Zhang, Min Gao, Junliang Yu +3
With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the grou…
Socially-Aware Self-Supervised Tri-Training for Recommendation
Junliang Yu, Hongzhi Yin, Min Gao +3
Self-supervised learning (SSL), which can automatically generate ground-truth samples from raw data, holds vast potential to improve recommender systems. Most existing SSL-based me…
Path-Based Reasoning over Heterogeneous Networks for Recommendation via Bidirectional Modeling
Junwei Zhang, Min Gao, Junliang Yu +3
Heterogeneous Information Network (HIN) is a natural and general representation of data in recommender systems. Combining HIN and recommender systems can not only help model user b…
Enhancing Social Recommendation with Adversarial Graph Convolutional Networks
Junliang Yu, Hongzhi Yin, Jundong Li +3
Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent repor…
Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective
Min Gao, Junwei Zhang, Junliang Yu +3
Recommender systems (RSs) now play a very important role in the online lives of people as they serve as personalized filters for users to find relevant items from an array of optio…
Generating Reliable Friends via Adversarial Training to Improve Social Recommendation
Junliang Yu, Min Gao, Hongzhi Yin +3
Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditi…