Search Results for author: Chaokun Wang

Found 11 papers, 5 papers with code

Graph Contrastive Learning with Generative Adversarial Network

no code implementations1 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.

Contrastive Learning Data Augmentation +3

PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation

no code implementations7 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.

Denoising Graph Representation Learning +1

Multi-behavior Self-supervised Learning for Recommendation

1 code implementation22 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.

Graph Neural Network Self-Supervised Learning

Instant Representation Learning for Recommendation over Large Dynamic Graphs

1 code implementation22 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.

Graph Neural Network Representation Learning

Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search

no code implementations4 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.

Knowledge Distillation

HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks

no code implementations3 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).

Recommendation Systems

Rethinking Temperature in Graph Contrastive Learning

no code implementations29 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.

Contrastive Learning Self-Supervised Learning

Network Embedding with Completely-imbalanced Labels

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.

Network Embedding

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