no code implementations • 9 Apr 2024 • Shen Gao, Yifan Wang, Jiabao Fang, Lisi Chen, Peng Han, Shuo Shang
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior. However, the lack of transparency in presenting recommendations can lead to user confusion.
no code implementations • 8 Apr 2024 • Shen Gao, Hao Li, Zhengliang Shi, Chengrui Huang, Quan Tu, Zhiliang Tian, Minlie Huang, Shuo Shang
The framework employs a novel 360{\deg} performance assessment method for multi-perspective performance evaluation with fine-grained assessment.
1 code implementation • 8 Mar 2024 • Hongda Sun, Yuxuan Liu, ChengWei Wu, Haiyu Yan, Cheng Tai, Xin Gao, Shuo Shang, Rui Yan
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems.
no code implementations • 5 Mar 2024 • Chuanqi Cheng, Quan Tu, Wei Wu, Shuo Shang, Cunli Mao, Zhengtao Yu, Rui Yan
Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas.
no code implementations • 4 Mar 2024 • Siqi Fan, Xin Jiang, Xiang Li, Xuying Meng, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang, Zhongyuan Wang
To answer this question, we first indicate that Not all Layers are Necessary during Inference by statistically analyzing the activated layers across tasks.
no code implementations • 28 Feb 2024 • Yang Cao, Shuo Shang, Jun Wang, Wei zhang
This paper explores providing explainability for session-based recommendation (SR) by path reasoning.
1 code implementation • 28 Oct 2023 • Hongda Sun, Weikai Xu, Wei Liu, Jian Luan, Bin Wang, Shuo Shang, Ji-Rong Wen, Rui Yan
To address these challenges, we propose DetermLR, a novel reasoning framework that formulates the reasoning process as a transformational journey from indeterminate premises to determinate ones.
no code implementations • 24 Oct 2023 • Yuxiang Wang, Xiao Yan, Chuang Hu, Fangcheng Fu, Wentao Zhang, Hao Wang, Shuo Shang, Jiawei Jiang
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features.
1 code implementation • 31 Aug 2023 • Qiang Huang, Jiawei Jiang, Xi Susie Rao, Ce Zhang, Zhichao Han, Zitao Zhang, Xin Wang, Yongjun He, Quanqing Xu, Yang Zhao, Chuang Hu, Shuo Shang, Bo Du
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.
1 code implementation • 20 Aug 2023 • Quan Tu, Chuanqi Chen, Jinpeng Li, Yanran Li, Shuo Shang, Dongyan Zhao, Ran Wang, Rui Yan
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency.
1 code implementation • 14 Apr 2023 • Yiqun Yao, Siqi Fan, Xiusheng Huang, Xuezhi Fang, Xiang Li, Ziyi Ni, Xin Jiang, Xuying Meng, Peng Han, Shuo Shang, Kang Liu, Aixin Sun, Yequan Wang
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.