Search Results for author: Min Gao

Found 14 papers, 4 papers with code

Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation

no code implementations8 Mar 2022 Yinghui Tao, Min Gao, Junliang Yu, Zongwei Wang, Qingyu Xiong, Xu Wang

To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths.

Auxiliary Learning Self-Supervised Learning

Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion

no code implementations19 Feb 2022 Shiqi Wang, Chongming Gao, Min Gao, Junliang Yu, Zongwei Wang, Hongzhi Yin

By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.

reinforcement-learning

Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

1 code implementation9 Sep 2021 Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, Hongzhi Yin

Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups.

Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack

no code implementations22 Jul 2021 Fan Wu, Min Gao, Junliang Yu, Zongwei Wang, Kecheng Liu, Xu Wange

To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects.

Recommendation Systems

Socially-Aware Self-Supervised Tri-Training for Recommendation

1 code implementation7 Jun 2021 Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, Nguyen Quoc Viet Hung

Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.

Contrastive Learning Recommendation Systems +2

BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions

1 code implementation29 Jan 2021 Yifan Wu, Min Gao, Min Zeng, Feiyang Chen, Min Li, Jie Zhang

Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.

Drug Discovery

Skeleton-based Approaches based on Machine Vision: A Survey

no code implementations23 Dec 2020 Jie Li, Binglin Li, Min Gao

Recently, skeleton-based approaches have achieved rapid progress on the basis of great success in skeleton representation.

Object Detection

Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges

no code implementations18 Oct 2020 Amrita Bhattacharjee, Kai Shu, Min Gao, Huan Liu

We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.

Misinformation

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

no code implementations17 Aug 2020 Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, Xun Chen

MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.

Arrhythmia Detection

Path-Based Reasoning over Heterogeneous Networks for Recommendation via Bidirectional Modeling

1 code implementation10 Aug 2020 Junwei Zhang, Min Gao, Junliang Yu, Linda Yang, Zongwei Wang, Qingyu Xiong

Despite their effectiveness, these models are often confronted with the following limitations: (1) Most prior path-based reasoning models only consider the influence of the predecessors on the subsequent nodes when modeling the sequences, and ignore the reciprocity between the nodes in a path; (2) The weights of nodes in the same path instance are usually assumed to be constant, whereas varied weights of nodes can bring more flexibility and lead to expressive modeling; (3) User-item interactions are noisy, but they are often indiscriminately exploited.

Recommendation Systems

Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning

no code implementations19 Apr 2020 Min Gao, Yukun Guo, Tristan T. Hormel, Jiande Sun, Thomas Hwang, Yali Jia

The reconstructed 6x6-mm angiograms have significantly lower noise intensity and better vascular connectivity than the original images.

Image Enhancement

Enhancing Social Recommendation with Adversarial Graph Convolutional Networks

no code implementations5 Apr 2020 Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.

Recommendation Systems

Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective

no code implementations5 Mar 2020 Min Gao, Junwei Zhang, Junliang Yu, Jundong Li, Junhao Wen, Qingyu Xiong

In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy.

Data Augmentation Recommendation Systems

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

no code implementations8 Sep 2019 Junliang Yu, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, Qinyong Wang

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.

Recommendation Systems

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