7 citations · 8 across the 3 of their papers we have counts for
5 papers
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…
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…