Search Results for author: Quanyu Dai

Found 26 papers, 12 papers with code

Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation

1 code implementation COLING 2022 Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiaoming Wu

We validate the effectiveness of PREC through both offline evaluation on public datasets and online A/B testing in an industrial application.

Click-Through Rate Prediction News Recommendation

Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

no code implementations14 Sep 2023 Jiaren Xiao, Quanyu Dai, Xiao Shen, Xiaochen Xie, Jing Dai, James Lam, Ka-Wai Kwok

To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels.

Contrastive Learning Domain Adaptation +2

Only Encode Once: Making Content-based News Recommender Greener

no code implementations27 Aug 2023 Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiao-Ming Wu

Large pretrained language models (PLM) have become de facto news encoders in modern news recommender systems, due to their strong ability in comprehending textual content.

News Recommendation Recommendation Systems +1

Out-of-distribution Detection with Implicit Outlier Transformation

1 code implementation9 Mar 2023 Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han

It leads to a min-max learning scheme -- searching to synthesize OOD data that leads to worst judgments and learning from such OOD data for uniform performance in OOD detection.

Out-of-Distribution Detection

A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction

no code implementations12 Nov 2022 Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Jie Sun

However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.

Generalization Bounds Imputation +1

Recommendation with User Active Disclosing Willingness

no code implementations25 Oct 2022 Lei Wang, Xu Chen, Quanyu Dai, Zhenhua Dong

Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production. Traditional recommender models mostly collect as comprehensive as possible user behaviors for accurate preference estimation.

Recommendation Systems

Debiased Recommendation with Neural Stratification

no code implementations15 Aug 2022 Quanyu Dai, Zhenhua Dong, Xu Chen

Debiased recommender models have recently attracted increasing attention from the academic and industry communities.

Multiple Robust Learning for Recommendation

no code implementations9 Jul 2022 Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu, Zhenhua Dong, Xiao-Hua Zhou, Peng Wu

Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate.

Imputation Recommendation Systems

BARS: Towards Open Benchmarking for Recommender Systems

5 code implementations19 May 2022 Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang

Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.

Benchmarking Recommendation Systems

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

1 code implementation23 Feb 2022 Yan Lyu, Sunhao Dai, Peng Wu, Quanyu Dai, yuhao deng, Wenjie Hu, Zhenhua Dong, Jun Xu, Shengyu Zhu, Xiao-Hua Zhou

To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.

Causal Inference Descriptive +2

On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges

no code implementations18 Jan 2022 Peng Wu, Haoxuan Li, yuhao deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou

Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.

Causal Inference Recommendation Systems

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

1 code implementation26 Sep 2021 Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, Xiuqiang He

While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored.

Collaborative Filtering Recommendation Systems

Top-N Recommendation with Counterfactual User Preference Simulation

no code implementations2 Sep 2021 Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, Jun Wang

To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.

Causal Inference counterfactual +1

Network Together: Node Classification via Cross-Network Deep Network Embedding

1 code implementation4 Jun 2020 Xiao Shen, Quanyu Dai, Sitong Mao, Fu-Lai Chung, Kup-Sze Choi

On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.

Domain Adaptation General Classification +2

Adversarial Deep Network Embedding for Cross-network Node Classification

2 code implementations18 Feb 2020 Xiao Shen, Quanyu Dai, Fu-Lai Chung, Wei Lu, Kup-Sze Choi

This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information.

Classification Domain Adaptation +3

Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs

1 code implementation26 Sep 2019 Qimai Li, Xiaotong Zhang, Han Liu, Quanyu Dai, Xiao-Ming Wu

Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that computes the representation of a node by aggregating those of its neighbors.

Attribute Clustering +3

Adversarial Training Methods for Network Embedding

1 code implementation30 Aug 2019 Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang

To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain.

Link Prediction Network Embedding +1

Network Together: Node Classification via Cross network Deep Network Embedding

1 code implementation22 Jan 2019 Xiao Shen, Quanyu Dai, Sitong Mao, Fu-Lai Chung, Kup-Sze Choi

On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.

Social and Information Networks

Adversarial Network Embedding

no code implementations21 Nov 2017 Quanyu Dai, Qiang Li, Jian Tang, Dan Wang

Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization.

Link Prediction Network Embedding +1

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