1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
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…
Augmenting Sequential Recommendation with Balanced Relevance and Diversity
Yizhou Dang, Jiahui Zhang, Yuting Liu +5
By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on au…
Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang +3
Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consiste…
Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation
Yuting Liu, Yizhou Dang, Yuliang Liang +4
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (…
Video and Audio are Images: A Cross-Modal Mixer for Original Data on Video-Audio Retrieval
Zichen Yuan, Qi Shen, Bingyi Zheng +3
Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia. Generally, the information from each modality exhibits distinct representations…