1 code implementation • 24 Mar 2024 • Taotian Pang, Xingyu Lou, Fei Zhao, Zhen Wu, Kuiyao Dong, Qiuying Peng, Yue Qi, Xinyu Dai
Specifically, we build \textit{user preference representations} and \textit{attribute fusion representations} upon the attribute information in knowledge graphs, which are utilized to enhance \textit{collaborative filtering} (CF) based user and item representations, respectively.
no code implementations • 22 Jan 2024 • Chao Song, Zhihao Ye, Qiqiang Lin, Qiuying Peng, Jun Wang
In practice, there are two prevailing ways, in which the adaptation can be achieved: (i) Multiple Independent Models: Pre-trained LLMs are fine-tuned a few times independently using the corresponding training samples from each task.
no code implementations • 4 Jan 2024 • Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang, Weiming Lu
Experiments conducted on a series of reasoning and translation tasks with different LLMs serve to underscore the effectiveness and generality of our strategy.
1 code implementation • 19 May 2023 • Zhe Chen, Hao Tan, Tao Wang, Tianrun Shen, Tong Lu, Qiuying Peng, Cheng Cheng, Yue Qi
The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks.
Ranked #2 on Graph Regression on PCQM4M-LSC (Validation MAE metric)
no code implementations • 5 May 2022 • Fan Zhang, Qiuying Peng, Yulin Wu, Zheng Pan, Rong Zeng, Da Lin, Yue Qi
Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods.