42 citations · 94 across the 31 of their papers we have counts for
5 papers · 1 filter
One for Dozens: Adaptive REcommendation for All Domains with Counterfactual Augmentation
Huishi Luo, Yiwen Chen, Yiqing Wu +2
Multi-domain recommendation (MDR) aims to enhance recommendation performance across various domains. However, real-world recommender systems in online platforms often need to handl…
FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning
Wei Chen, Meng Yuan, Zhao Zhang +4
As trustworthy AI continues to advance, the fairness issue in recommendations has received increasing attention. A recommender system is considered unfair when it produces unequal…
E5-V: Universal Embeddings with Multimodal Large Language Models
Ting Jiang, Minghui Song, Zihan Zhang +6
Multimodal large language models (MLLMs) have shown promising advancements in general visual and language understanding. However, the representation of multimodal information using…
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Ting Jiang, Shaohan Huang, Shengyue Luo +8
Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models. In this paper, we analyze the impact of low-rank updating, as implemented in LoRA…
Improving Domain Adaptation through Extended-Text Reading Comprehension
Ting Jiang, Shaohan Huang, Shengyue Luo +8
To enhance the domain-specific capabilities of large language models, continued pre-training on a domain-specific corpus is a prevalent method. Recent work demonstrates that adapti…