10 citations · 24 across the 7 of their papers we have counts for
11 papers
Addressing the Extreme Cold-Start Problem in Group Recommendation
Guo linxin, Tao yinghui, Gao Min +3
The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems…
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…
Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion
Shiqi Wang, Chongming Gao, Min Gao +3
With the increasingly fierce market competition, offering a free trial has become a potent stimuli strategy to promote products and attract users. By providing users with opportuni…
Self-Supervised Graph Co-Training for Session-based Recommendation
Xin Xia, Hongzhi Yin, Junliang Yu +2
Session-based recommendation targets next-item prediction by exploiting user behaviors within a short time period. Compared with other recommendation paradigms, session-based recom…
Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack
Fan Wu, Min Gao, Junliang Yu +3
To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects. Primitive attacks are highly feasible…
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…