78 citations · 179 across the 10 of their papers we have counts for
10 papers
DaV-Gen: End-to-End Generative Retrieval via Draft-and-Verify
Meng Zhao, Chunmei Liu, Qinyong Wang
Mainstream industrial information retrieval systems (e.g., search and recommendation) are usually built upon Multi-Stage Cascade Architectures (MCAs), which balance effectiveness a…
LLMRec: Large Language Models with Graph Augmentation for Recommendation
Wei Wei, Xubin Ren, Jiabin Tang +6
The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. Howev…
Efficient On-Device Session-Based Recommendation
Xin Xia, Junliang Yu, Qinyong Wang +3
On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing pro…
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
Xin Xia, Hongzhi Yin, Junliang Yu +3
Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user…
Fast-adapting and Privacy-preserving Federated Recommender System
Qinyong Wang, Hongzhi Yin, Tong Chen +3
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem. Rece…
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Junliang Yu, Hongzhi Yin, Jundong Li +3
Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models explo…
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Xin Xia, Hongzhi Yin, Junliang Yu +3
Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent…
Overcoming Data Sparsity in Group Recommendation
Hongzhi Yin, Qinyong Wang, Kai Zheng +2
It has been an important task for recommender systems to suggest satisfying activities to a group of users in people's daily social life. The major challenge in this task is how to…
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…
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Hao Wang, Yanmei Fu, Qinyong Wang +3
Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current rese…