Ruiming Tang

发表

TRB 3 / 2014 Conditioning Probabilistic Relational Data with Referential Constraints

Dongxu Shao, Ruiming Tang, M. Lamine, 2014 .

Cross Pairwise Ranking for Unbiased Item Recommendation

Xiangnan He, Xiang Wang, Ruiming Tang, 2022, WWW.

TGCN: Tag Graph Convolutional Network for Tag-Aware Recommendation

Bo Chen, Dong Wang, Xiuqiang He, 2020, CIKM.

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

Weinan Zhang, Weijun Hong, Zhenguo Li, 2020, IJCAI.

RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows

Weinan Zhang, Rui Zhang, Ruiming Tang, 2022, SIGIR.

RankFlow

Weinan Zhang, Rui Zhang, Ruiming Tang, 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

On Effective Scheduling of Model-based Reinforcement Learning

Weinan Zhang, Xingzhi Zhang, Zhenguo Li, 2021, NeurIPS.

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction

Ruiming Tang, Yingxue Zhang, Hong Zhu, 2022, International Conference on Information and Knowledge Management.

ReLoop

Ruiming Tang, Quanyu Dai, Zhenhua Dong, 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems

Jieming Zhu, Ruiming Tang, Quanyu Dai, 2022, SIGIR.

CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation

Ruiming Tang, Zhenhua Dong, Zhirong Liu, 2022, KDD.

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Dong Li, Ruiming Tang, Yingxue Zhang, 2022, ACM Trans. Inf. Syst..

LCD: Adaptive Label Correction for Denoising Music Recommendation

Junjie Ye, Shutao Xia, Jieming Zhu, 2022, CIKM.

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

Irwin King, Ruiming Tang, Chen Ma, 2022, KDD.

Multi-Behavior Sequential Transformer Recommender

Ruiming Tang, Enming Yuan, Wei Guo, 2022, SIGIR.

Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation pdf

Irwin King, Ruiming Tang, Xiuqiang He, 2021, ArXiv.

An Effective Post-training Embedding Binarization Approach for Fast Online Top-K Passage Matching

Irwin King, Ruiming Tang, Yifei Zhang, 2022, AACL.

Memorize, Factorize, or be Naive: Learning Optimal Feature Interaction Methods for CTR Prediction

Rui Zhang, Ruiming Tang, Xiuqiang He, 2021, 2022 IEEE 38th International Conference on Data Engineering (ICDE).

AIM: Automatic Interaction Machine for Click-Through Rate Prediction pdf

Zhenguo Li, Ruiming Tang, Xiuqiang He, 2021, IEEE Transactions on Knowledge and Data Engineering.

AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction

Stefanos Zafeiriou, Zhenguo Li, Huifeng Guo, 2020, SIGIR.

AutoFIS

Weinan Zhang, Yong Yu, B. Liu, 2020, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

Bin Liu, Yong Yu, Weinan Zhang, 2020, KDD.

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Yunming Ye, Zhenguo Li, Huifeng Guo, 2017, IJCAI.

A Framework for Conditioning Uncertain Relational Data

Stéphane Bressan, Huayu Wu, Reynold Cheng, 2012, DEXA.

Multi-graph Convolution Collaborative Filtering

Mark Coates, Chen Ma, Huifeng Guo, 2019, 2019 IEEE International Conference on Data Mining (ICDM).

Multi-Level Interaction Reranking with User Behavior History

Weinan Zhang, Jieming Zhu, Ruiming Tang, 2022, SIGIR.

Context-aware Reranking with Utility Maximization for Recommendation

Weinan Zhang, Ruiming Tang, Xiuqiang He, 2021, ArXiv.

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks

Dengcheng Zhang, Mark Coates, Huifeng Guo, 2020, KDD.

Neural Re-ranking in Multi-stage Recommender Systems: A Review

Weinan Zhang, Fei Sun, Ruiming Tang, 2022, IJCAI.

Multi-Task Deep Recommender Systems: A Survey

Ha T. Lam, Ruiming Tang, Xiang Zhao, 2023, ArXiv.

A Comprehensive Survey on Automated Machine Learning for Recommendations

Ruiming Tang, Yejing Wang, Wenqi Fan, 2022, ACM Transactions on Recommender Systems.

Automated Machine Learning for Deep Recommender Systems: A Survey

Xiangyu Zhao, Ruiming Tang, Yejing Wang, 2022, ArXiv.

Dual Graph enhanced Embedding Neural Network for CTR Prediction

Zhirong Liu, Huifeng Guo, Ruiming Tang, 2021, KDD.

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

Weinan Zhang, Xinyi Dai, Ruiming Tang, 2020, SIGIR.

Deep Learning for Click-Through Rate Estimation

Weinan Zhang, Ruiming Tang, Wei Guo, 2021, IJCAI.

Personalized Re-ranking for Improving Diversity in Live Recommender Systems

X. Zhang, Ruiming Tang, Zhenhua Dong, 2020, ArXiv.

State representation modeling for deep reinforcement learning based recommendation

Yunming Ye, Weinan Zhang, Xutao Li, 2020, Knowl. Based Syst..

Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction

Rui Zhang, Rui Li, Ruiming Tang, 2022, AAAI.

ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table

Huifeng Guo, Ruiming Tang, Xiuqiang He, 2021, SIGIR.

AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction

Zhenguo Li, Bin Liu, Ruiming Tang, 2020, CIKM.

Hierarchical Imitation Learning via Subgoal Representation Learning for Dynamic Treatment Recommendation

Xiaofeng He, Ruiming Tang, Xiuqiang He, 2022, WSDM.

A Survey on User Behavior Modeling in Recommender Systems

Ruiming Tang, Wei Guo, Yingxue Zhang, 2023, IJCAI.

Neighbor Interaction Aware Graph Convolution Networks for Recommendation

Mark Coates, Huifeng Guo, Xiuqiang He, 2020, SIGIR.

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

Wei Guo, Mark Coates, Huifeng Guo, 2020, CIKM.

IntTower: The Next Generation of Two-Tower Model for Pre-Ranking System

Sujian Li, Ruiming Tang, Wei Guo, 2022, CIKM.

Learning to Build High-Fidelity and Robust Environment Models

Zhenguo Li, Ruiming Tang, Yimin Huang, 2021, ECML/PKDD.

Large-scale Interactive Recommendation with Tree-structured Policy Gradient pdf

Yong Yu, Weinan Zhang, Han Cai, 2018, AAAI.

Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

Zhirong Liu, Ruiming Tang, Xiuqiang He, 2021, CIKM.

An Embedding Learning Framework for Numerical Features in CTR Prediction

Zhenguo Li, Huifeng Guo, Ruiming Tang, 2020, KDD.

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Bin Liu, Huifeng Guo, Ruiming Tang, 2019, WWW.

AutoAssign: Automatic Shared Embedding Assignment in Streaming Recommendation

Ruiming Tang, Fengyi Song, Xiangyu Zhao, 2022, 2022 IEEE International Conference on Data Mining (ICDM).

Learning to Retrieve User Behaviors for Click-Through Rate Estimation

Weinan Zhang, Ruiming Tang, Xiuqiang He, 2023, ACM Transactions on Information Systems.

MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction

Rui Zhang, Ruiming Tang, Wei Guo, 2021, 2022 IEEE 38th International Conference on Data Engineering (ICDE).

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning pdf

Guangyong Chen, Ruiming Tang, Pheng Ann Heng, 2021, ArXiv.

An Adversarial Imitation Click Model for Information Retrieval

Weinan Zhang, Jianye Hao, Shuai Li, 2021, WWW.

Conditioning Probabilistic Relational Data with Referential Constraints

Huayu Wu, Dongxu Shao, Ruiming Tang, 2014, DASFAA Workshops.

Numerical Feature Representation with Hybrid N-ary Encoding

Rui Zhang, Ruiming Tang, Wei Guo, 2022, CIKM.

Retrieval & Interaction Machine for Tabular Data Prediction

Zhirong Liu, Ruiming Tang, Xiuqiang He, 2021, KDD.

A Practical Incremental Method to Train Deep CTR Models pdf

Yichao Wang, Huifeng Guo, Ruiming Tang, 2020, ArXiv.

Unsupervised Learning Style Classification for Learning Path Generation in Online Education Platforms

Rui Zhang, Ruiming Tang, Huifeng Guo, 2022, KDD.

Personalized Re-ranking with Item Relationships for E-commerce

P. Heng, Weiwen Liu, Ruiming Tang, 2020, CIKM.

Balancing Between Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Feng Liu, Pheng-Ann Heng, Weiwen Liu, 2020, PAKDD.

Conditioning Probabilistic XML (Extended Version)

Stéphane Bressan, Pierre Senellart, Dongxu Shao, 2013 .

Optimizing Ranking Algorithm in Recommender System via Deep Reinforcement Learning

Feng Liu, Ruiming Tang, Jianhua Han, 2019, 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM).

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling pdf

Feng Liu, Yunming Ye, Xutao Li, 2018, ArXiv.

Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation

Weinan Zhang, Jian Shen, Menghui Zhu, 2023, AAAI.

Get a Sample for a Discount - Sampling-Based XML Data Pricing

Stéphane Bressan, Pierre Senellart, Antoine Amarilli, 2014, DEXA.

How Can Recommender Systems Benefit from Large Language Models: A Survey

Weinan Zhang, Ruiming Tang, Bo Chen, 2023, ACM Transactions on Information Systems.

An efficient and truthful pricing mechanism for team formation in crowdsourcing markets

Stéphane Bressan, Qing Liu, Tie Luo, 2015, 2015 IEEE International Conference on Communications (ICC).

Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems

Jianye Hao, Ruiming Tang, Chen Ma, 2023, Proceedings of the AAAI Conference on Artificial Intelligence.

GraphSAIL

M. Coates, Ruiming Tang, Wei Guo, 2020, Proceedings of the 29th ACM International Conference on Information & Knowledge Management.

Field-aware probabilistic embedding neural network for CTR prediction

Shengyu Zhang, Huifeng Guo, Ruiming Tang, 2018, RecSys.

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction pdf

Zhirong Liu, Ruiming Tang, Xiuqiang He, 2021, ArXiv.

Dynamically Expandable Graph Convolution for Streaming Recommendation

Ruiming Tang, Chen Ma, Yingxue Zhang, 2023, WWW.

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

Xue Liu, Mark Coates, Yingxue Zhang, 2020, KDD.

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

Weinan Zhang, Yong Yu, Jun Wang, 2020, CIKM.

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Ruiming Tang, Haolun Wu, Xue Liu, 2022, CIKM.

MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction

Weinan Zhang, Ruiming Tang, Wei Guo, 2023, KDD.

ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop

Weinan Zhang, Jieming Zhu, Ruiming Tang, 2023, KDD.

CTRL: Connect Tabular and Language Model for CTR Prediction

Ruiming Tang, Bo Chen, Luyao Hou, 2023, ArXiv.

AutoML for Deep Recommender Systems: Fundamentals and Advances

Ruiming Tang, Yejing Wang, Wenqi Fan, 2023, WSDM.

A Brief History of Recommender Systems

Ruiming Tang, Zhenhua Dong, Jun Xu, 2022, ArXiv.

Click-through rate prediction using transfer learning with fine-tuned parameters

Ruiming Tang, Xiuqiang He, Zhirong Liu, 2022, Inf. Sci..

Beyond Relevance Ranking: A General Graph Matching Framework for Utility-Oriented Learning to Rank

Weinan Zhang, Yong Yu, Jun Wang, 2021, ACM Trans. Inf. Syst..

AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction pdf

Bo Chen, Zhenguo Li, Huifeng Guo, 2020, ArXiv.

End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding

Yunming Ye, Feng Liu, Xutao Li, 2020, WSDM.

PAL

Ruiming Tang, Q. Liu, Huifeng Guo, 2019, Proceedings of the 13th ACM Conference on Recommender Systems.

Order-aware Embedding Neural Network for CTR Prediction

Wei Guo, Wen Yang, Huifeng Guo, 2019, SIGIR.

DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction pdf

Yunming Ye, Zhenguo Li, Huifeng Guo, 2018, ArXiv.

Holistic Neural Network for CTR Prediction

Yunming Ye, Huifeng Guo, Ruiming Tang, 2017, WWW.

Semi-deterministic and Contrastive Variational Graph Autoencoder for Recommendation

Chenghua Lin, Ruiming Tang, Yue Ding, 2021, CIKM.

Robust Causal Inference for Recommender System to Overcome Noisy Confounders

Ruiming Tang, Quanyu Dai, Zhenhua Dong, 2023, SIGIR.

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

Weinan Zhang, Rui Zhang, Jieming Zhu, 2023, ArXiv.

Improving Knowledge Tracing with Collaborative Information

Weinan Zhang, Yong Yu, Jian Shen, 2022, WSDM.

Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction

Jieming Zhu, Ruiming Tang, Wei Guo, 2023, ArXiv.

On-device Integrated Re-ranking with Heterogeneous Behavior Modeling

Weinan Zhang, Y. Zhu, Rui Zhang, 2023, KDD.

Personalized Diversification for Neural Re-ranking in Recommendation

Weinan Zhang, Shuai Li, Rui Zhang, 2023, 2023 IEEE 39th International Conference on Data Engineering (ICDE).

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

Weinan Zhang, Yong Yu, Guilin Li, 2022, ArXiv.

Task Adaptive Multi-learner Network for Joint CTR and CVR Estimation

Chuhan Wu, Rui Zhang, Ruiming Tang, 2023, WWW.

PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation

Jieming Zhu, Ruiming Tang, Liangcai Su, 2022, WWW.

Balancing Utility and Exposure Fairness for Integrated Ranking with Reinforcement Learning

Ruiming Tang, Weiwen Liu, Yifan Liu, 2022, CIKM.

Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data

Yong Yu, Weinan Zhang, Huifeng Guo, 2018, ACM Trans. Inf. Syst..

Single-shot Feature Selection for Multi-task Recommendations

Ruiming Tang, Yejing Wang, Zhenhua Dong, 2023, SIGIR.

AutoGen: An Automated Dynamic Model Generation Framework for Recommender System

Weinan Zhang, Ruiming Tang, Xiangyu Zhao, 2023, WSDM.

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

Ruiming Tang, Wei Guo, Haolun Wu, 2022, 2023 IEEE 39th International Conference on Data Engineering (ICDE).

AutoTransfer: Instance Transfer for Cross-Domain Recommendations

Ruiming Tang, Xiangyu Zhao, Bo Chen, 2023, SIGIR.

PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations

Rui Zhang, Ruiming Tang, Xiangyu Zhao, 2023, SIGIR.

AutoHash: Learning Higher-Order Feature Interactions for Deep CTR Prediction

Bin Liu, Stefanos Zafeiriou, Jun Wang, 2022, IEEE Transactions on Knowledge and Data Engineering.

Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification

Feng Liu, Yunming Ye, Xutao Li, 2018, DASFAA.

Integrated Ranking for News Feed with Reinforcement Learning

Menghui Zhu, Ruiming Tang, Weinan Zhang, 2023, WWW.

PAL: a position-bias aware learning framework for CTR prediction in live recommender systems

Huifeng Guo, Ruiming Tang, Qing Liu, 2019, RecSys.

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

Ruiming Tang, Bo Chen, Yichao Wang, 2023, ArXiv.

A Graph-Enhanced Click Model for Web Search

Yong Yu, Jianye Hao, Ruiming Tang, 2021, SIGIR.

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

Ruiming Tang, Fengyi Song, Xiangyu Zhao, 2023, Knowledge and Information Systems.

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction

Ruiming Tang, Yingxue Zhang, Hong Zhu, 2022, CIKM.

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

Weinan Zhang, Ruiming Tang, Bo Chen, 2023, ArXiv.

CTRL: Connect Collaborative and Language Model for CTR Prediction

Ruiming Tang, Bo Chen, Luyao Hou, 2023, 2306.02841.

Large-Scale Interactive Recommendation With Tree-Structured Reinforcement Learning

Weinan Zhang, Ruiming Tang, Xiuqiang He, 2023, IEEE Transactions on Knowledge and Data Engineering.

Top-aware reinforcement learning based recommendation

Yunming Ye, Xutao Li, Huifeng Guo, 2020, Neurocomputing.

Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation

Jieming Zhu, Ruiming Tang, Liangcai Su, 2023, SIGIR.

Hierarchical Projection Enhanced Multi-behavior Recommendation

Rui Zhang, Ruiming Tang, Wei Guo, 2023, KDD.

FINAL: Factorized Interaction Layer for CTR Prediction

Jieming Zhu, Ruiming Tang, Quanyu Dai, 2023, SIGIR.

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

Weinan Zhang, Rui Zhang, Jieming Zhu, 2023, ArXiv.

Compressed Interaction Graph based Framework for Multi-behavior Recommendation

Rui Zhang, Ruiming Tang, Enming Yuan, 2023, WWW.

Learning to Retrieve User Behaviors for Click-through Rate Estimation

Weinan Zhang, Ruiming Tang, Xiuqiang He, 2023, ACM Trans. Inf. Syst..

AutoGroup

B. Liu, S. Zafeiriou, Zhenguo Li, 2020, Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.

ModularNAS: Towards Modularized and Reusable Neural Architecture Search

Weinan Zhang, Yong Yu, Guilin Li, 2021, MLSys.

Collaborative Filtering with Graph-Based Implicit Feedback

Yong Yu, Weinan Zhang, Ruiming Tang, 2017, 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC).

2015 Ieee International Conference on Big Data (big Data) Mining Target Users for Online Marketing Based on App Store Data

Qiang Yang, Mingxuan Yuan, Ruiming Tang, 2022 .

Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks

Ruiming Tang, Enming Yuan, Wei Guo, 2022, CIKM.

Hierarchical Imitation Learning via Subgoal Representation Learning for Dynamic Treatment Recommendation

Xiaofeng He, Ruiming Tang, Xiuqiang He, 2022, WSDM.

Diffusion Augmentation for Sequential Recommendation

Ruiming Tang, Huifeng Guo, Zhaochen Du, 2023, CIKM.