3 papers
cs.CL2025
Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product
Pengxiang Lan, Haoyu Xu, Enneng Yang +4
Prompt tuning (PT) offers a cost-effective alternative to fine-tuning large-scale pre-trained language models (PLMs), requiring only a few parameters in soft prompt tokens added be…
cs.LG2025
Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
Chu Zhao, Enneng Yang, Yuliang Liang +3
The distributionally robust optimization (DRO)-based graph neural network methods improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the model's…
cs.IR2024
Data Augmentation for Sequential Recommendation: A Survey
Yizhou Dang, Enneng Yang, Yuting Liu +4
As an essential branch of recommender systems, sequential recommendation (SR) has received much attention due to its well-consistency with real-world situations. However, the wides…