83 citations · 125 across the 7 of their papers we have counts for
7 papers
Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation
Xin Xia, Junliang Yu, Guandong Xu +1
On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy. To stay current with evolving u…
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…
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
Junliang Yu, Xin Xia, Tong Chen +3
Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The underlying principle of CL-based recommendation models is to ensure t…
API Usage Recommendation via Multi-View Heterogeneous Graph Representation Learning
Yujia Chen, Xiaoxue Ren, Cuiyun Gao +3
Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for dev…
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…
Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
Junliang Yu, Hongzhi Yin, Xin Xia +3
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is…
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…