1 code implementation • 29 Jul 2024 • Weichen Li, Xiaotong Huang, Jianwu Zheng, Zheng Wang, Chaokun Wang, Li Pan, Jianhua Li
To illustrate the usage of rLLM, we introduce a simple RTL method named \textbf{BRIDGE}.
Ranked #1 on Classification on TACM12K
no code implementations • 1 Aug 2023 • Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang song, Kun Gai
Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning.
no code implementations • 7 Jun 2023 • Ziyang Liu, Chaokun Wang, Jingcao Xu, Cheng Wu, Kai Zheng, Yang song, Na Mou, Kun Gai
Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users.
1 code implementation • 22 May 2023 • Jingcao Xu, Chaokun Wang, Cheng Wu, Yang song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, Kun Gai
Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task.
1 code implementation • 22 May 2023 • Cheng Wu, Chaokun Wang, Jingcao Xu, Ziwei Fang, Tiankai Gu, Changping Wang, Yang song, Kai Zheng, Xiaowei Wang, Guorui Zhou
Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models.
no code implementations • 4 Oct 2022 • Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu, Liqun Yang
In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models.
no code implementations • 3 Aug 2022 • Tiankai Gu, Chaokun Wang, Cheng Wu, Jingcao Xu, Yunkai Lou, Changping Wang, Kai Xu, Can Ye, Yang song
One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i. e., relationship).
no code implementations • 29 Sep 2021 • Ziyang Liu, Hao Feng, Chaokun Wang
In this paper, we investigate and discuss what a good representation should be for a general loss (InfoNCE) in graph contrastive learning.
no code implementations • 23 Mar 2021 • Zheng Wang, Ruihang Shao, Changping Wang, Changjun Hu, Chaokun Wang, Zhiguo Gong
Zero-shot graph embedding is a major challenge for supervised graph learning.
2 code implementations • IEEE Transactions on Knowledge and Data Engineering 2020 • Zheng Wang, Xiaojun Ye, Chaokun Wang, Jian Cui, Philip S. Yu
Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research.
1 code implementation • The Thirty-Second AAAI Conference on Artificial Intelligence 2018 • ZhengWang, Xiaojun Ye, Chaokun Wang, YuexinWu, ChangpingWang, Kaiwen Liang
To alleviate this, we propose a novel semi-supervised network embedding method, termed Relaxed Similarity and Dissimilarity Network Embedding (RSDNE).