Link Search Menu Expand Document
Chen's Homepage

Conference Publications

[40] Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, Chen Ma, “Diffusion-based Contrastive Learning for Sequential Recommendation”, in the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024, acceptance rate: 347/1496=23%), Boise, USA, Oct 2024.
[Paper]

[39] Bowei He, Chen Ma, “Interpretable Triplet Importance for Personalized Ranking”, in the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024, acceptance rate: 347/1496=23%), Boise, USA, Oct 2024.
[Paper]

[38] Shengyin Sun, Chen Ma, “Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation”, in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024, acceptance rate: 24%), Vilnius Lithuania, Sep 2024.
[Paper]

[37] Bowei He, Yunpeng Weng, Xing Tang, Ziqiang Cui, Zexu Sun, Liang Chen, Xiuqiang He, Chen Ma, “Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing”, in the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024 ADS Track), Barcelona, Spain, Aug 2024.
[Paper]

[36] Chengming Hu, Haolun Wu, Xuan Li, Chen Ma, Xi Chen, Jun Yan, Boyu Wang, Xue Liu, “Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation”, in the Twelfth International Conference on Learning Representations (ICLR 2024, acceptance rate: 2260/7262=31%), Vienna, Austria, May 2024.
[Paper]

[35] Ziqiang Cui, Xing Tang, Yang Qiao, Bowei He, Liang Chen, Xiuqiang He, Chen Ma, “Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks”, in the SIAM International Conference on Data Mining (SDM 2024, acceptance rate: 98/416=29.2%), Houston, USA, Apr 2024.
[Paper]

[34] Zexu Sun, Bowei He, Jinxin Liu, Shuai Zhang, Xu Chen, Chen Ma, “Offline Imitation Learning with Variational Counterfactual Reasoning”, in the 27th Conference on Neural Information Processing Systems (NeurIPS 2023, acceptance rate: 26.1%), New Orleans, USA, Dec 2023.
[Paper]

[33] Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Weihong Luo, Xiuqiang He, Xue Liu, “Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network”, in the 27th Conference on Neural Information Processing Systems (NeurIPS 2023, acceptance rate: 26.1%), New Orleans, USA, Dec 2023.
[Paper]

[32] Zexu Sun, Bowei He, Ming Ma, Jiakai Tang, Yuchen Wang, Chen Ma, Dugang Liu, “Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization”, in the 23rd IEEE International Conference on Data Mining (ICDM 2023, acceptance rate: 200/1003=19.9%), Shanghai, China, Dec 2023.
[Paper]

[31] Bowei He, Xu He, Renrui Zhang, Yingxue Zhang, Ruiming Tang, Chen Ma, “Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System”, in the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023, acceptance rate: 354/1472=24%), Birmingham, UK, Oct 2023.
[Paper]

[30] Sichun Luo, Chen Ma, Yuanzhang Xiao, Linqi Song, “Improving Long-Tail Item Recommendation Performance with Graph Augmentation”, in the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023, acceptance rate: 354/1472=24%), Birmingham, UK, Oct 2023.
[Paper]

[29] Yejing Wang, Shen Ge, Xiangyu Zhao, Xian Wu, Tong Xu, Chen Ma, Zhi Zheng, “Doctor Specific Tag Recommendation for Online Medical Record Management”, in the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023 ADS Track), Long Beach, USA, Aug 2023.
[Paper]

[28] Chengmei Yang, Bowei He, Yimeng Wu, Chao Xing, Lianghua He, Chen Ma, “MMEL: A Joint Learning Framework for Multi-Mention Entity Linking”, in the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023, acceptance rate: 243/778=31.2%), Pittsburgh, USA, Aug 2023.
[Paper]

[27] Yankai Chen, Yifei Zhang, Menglin Yang, Zixing Song, Chen Ma， Irwin King, “WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering”, in the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), Taipei, Jul 2023.
[Paper]

[26] Yinan Mao, Bowei He, Shiji Zhou, Chen Ma, Zhi Wang, “Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling”, in the IEEE International Conference on Multimedia & Expo (ICME 2023), Brisbane, Australia, Jul 2023.
[Paper]

[25] Chengmei Yang, Shuai Jiang, Bowei He, Chen Ma, Lianghua He, “Mutually Guided Few-shot Learning for Relational Triple Extraction”, in the 48th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes Island, Greece, Jun 2023.
[Paper] [Code]

[24] Xiong-Hui Chen, Bowei He, Yang Yu, Qingyang Li, Zhiwei (Tony) Qin, Wenjie Shang, Jieping Ye, Chen Ma, “Sim2Rec: A Simulator-based Decision-Making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems”, in the 39th IEEE International Conference on Data Engineering (ICDE 2023, Industry and Applications Track), Anaheim, USA, Apr 2023.
[Paper]

[23] Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma, “Dynamically Expandable Graph Convolution for Streaming Recommendation”, in the 2023 ACM Web Conference (TheWebConf 2023, acceptance rate: 365/1900=19.2%), Austin, USA, May 2023.
[Paper] [Code]

[22] Yue Cui, Chen Ma, Kai Zheng, Lei Chen, Xiaofang Zhou, “Controllable Universal Fair Representation Learning”, in the 2023 ACM Web Conference (TheWebConf 2023, acceptance rate: 365/1900=19.2%), Austin, USA, May 2023.
[Paper]

[21] Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates, “Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation”, in the 39th IEEE International Conference on Data Engineering (ICDE 2023), Anaheim, USA, Apr 2023.
[Paper]

[20] Yuening Wang, Yingxue Zhang, Antonios Valkanas, Ruiming Tang, Chen Ma, Jianye Hao, Mark Coates, “Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems”, in the 37th AAAI Conference on Artificial Intelligence (AAAI 2023), Washington, USA, Feb 2023.
[Paper]

[19] Dan Liu, Xi Chen, Chen Ma, Xue Liu, “Hyperspherical Quantization: Toward Smaller and More Accurate Models”, in the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023), Waikoloa, USA, Jan 2023.
[Paper]

[18] Minyu Chen, Guoqiang Li, Chen Ma, Jingyang Li, Hongfei Fu, “Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub Repositories”, in the Proceedings of the 29th International Conference on Computational Linguistics (COLING 2022, acceptance rate: 522/1563=33.4%), Gyeongju, Korea, Oct 2022.
[Paper]

[17] Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates, “Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation”, in the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022, acceptance rate: N\A), Atlanta, USA, Oct 2022.
[Paper]

[16] Yankai Chen, Huifeng Guo, Yingxue Zhang, Chen Ma, Ruiming Tang, Jingjie Li, Irwin King, “Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation”, in the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022 Research Track, acceptance rate: 254/1695=15.0%), Washington, USA, Aug 2022.
[Paper]

[15] Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu, “Joint Multisided Exposure Fairness for Recommendation”, in the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022, acceptance rate: 161/794 = 20%), Madrid, Spain, Jul 2022.
[Paper] [Code]

[14] Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung, “TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion”, in the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021, acceptance rate: 151/720 = 21%), Montreal, Canada, Jul 2021.
[Paper]

[13] Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates, “Knowledge-Enhanced Top-K Recommendation in Poincaré Ball”, in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021, acceptance rate: 1692/7911 = 21.4%), Virtual, Feb 2021.
[Paper]

[12] Peng Kang, Jianping Zhang, Chen Ma, Guiling Sun, “ATM: Attentional Text Matting”, in the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV 2021), Virtual, Jan 2021.
[Paper]

[11] Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates, “Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation”, in the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020 Research Track, acceptance rate: 216/1279=16.9%), San Diego, USA, Aug 2020.
[Paper]

[10] Jianing Sun, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Xiuqiang He, Chen Ma, Mark Coates, “Neighbor Interaction Aware Graph Convolution Networks for Recommendation”, in the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020, acceptance rate: 147/555 = 26%), Xi’an, China, Jul 2020.
[Paper]

[9] Hang Li, Chen Ma, Wei Xu, Xue Liu, “Feature Statistics Guided Efficient Filter Pruning”, in the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020, acceptance rate: 592/4717 = 12.6%), Yokohama, Japan, Jul 2020.
[Paper]

[8] Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates, “Memory Augmented Graph Neural Networks for Sequential Recommendation”, in the 34th AAAI Conference on Artificial Intelligence (AAAI 2020, acceptance rate: 1591/7737 = 20.6%), New York, USA, Feb 2020.
[Paper]

[7] Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He, “Multi-Graph Convolution Collaborative Filtering”, in the 19th IEEE International Conference on Data Mining (IEEE ICDM 2019 short paper, acceptance rate: 194/1046 = 18.5%), Beijing, China, Nov 2019.
[Paper]

[6] Chen Ma, Peng Kang, Xue Liu, “Hierarchical Gating Networks for Sequential Recommendation”, in the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019 Research Track, acceptance rate: ~170/~1200=14%), Anchorage, USA, Aug 2019.
[Paper] [Code]

[5] Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, Xue Liu, “Gated Attentive-Autoencoder for Content-Aware Recommendation”, in the 12th ACM International Conference on Web Search and Data Mining (WSDM 2019, acceptance rate: 84/511=16%), Melbourne, Australia, Feb 2019.
[Paper] [Slides] [Code]

[4] Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu, “Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence”, in the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018, acceptance rate: 147/862=17%), Turin, Italy, Oct 2018.
[Paper] [Slides] [Code]

[3] Landu Jiang, Yu Hua, Chen Ma, Xue Liu, “SunChase: Energy-Efficient Route Planning for Solar-Powered EVs”, in the 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017, acceptance rate: 16.8%), Atlanta, USA, Jun 2017.
[Paper]

[2] Xi Chen, Chen Ma, Michel Allegue, Xue Liu, “Taming the Inconsistency of Wi-Fi Fingerprints for Device-Free Passive Indoor Localization”, in the 36th IEEE International Conference on Computer Communications (INFOCOM 2017, acceptance rate: 20.9%), Atlanta, USA, May 2017.
[Paper]

[1] Jiehao Chen, Chen Ma, Zizhen Yan, Bo Chen, Yu Shen, Yu Liang, “Defensive strategy of the goalkeeper based on the 3D vision and field division for the middle-size league of robocup”, in the 2013 IEEE International Conference on Granular Computing (GrC 2013), Beijing, China, Dec 2013.

Journal Publications

[5] Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Bowei He, Bhaskar Mitra, Xue Liu, “Result Diversification in Search and Recommendation: A Survey”, in IEEE Transactions on Knowledge and Data Engineering (TKDE), Apr 2024.
[Paper]

[4] Jiyun Shi, Zhimeng Yuan, Wenxuan Guo, Chen Ma, Jiehao Chen, Meihui Zhang, “Knowledge-Graph-Enabled Biomedical Entity Linking: A Survey”, in Springer World Wide Web (World Wide Web), Jan 2023.
[Paper][PDF]

[3] Ximing Li, Chen Ma, Guozheng Li, Peng Xu, Chi Harold Liu, Ye Yuan, Guoren Wang, “Meta Auxiliary Learning for Top-K Recommendation”, in IEEE Transactions on Knowledge and Data Engineering (TKDE), Nov 2022.
[Paper]

[2] Hang Li, Chen Ma, Xi Chen, Xue Liu, “Dynamic Consolidation for Continual Learning”, in Neural Computation, Oct 2022.
[Paper]

[1] Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu, “A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation”, in ACM Transactions on Information Systems (TOIS), Aug 2022.
[Paper]

Preprint

[5] Fuyuan Lyu, Xing Tang, Dugang Liu, Haolun Wu, Chen Ma, Xiuqiang He, Xue Liu, “Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives”, Feb 2023.
[Paper]

[4] Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Fernando Diaz, Xue Liu, “A Survey of Diversification Techniques in Search and Recommendation”, Jan 2023.
[Paper]

[3] Can Chen, Xi Chen, Chen Ma, Zixuan Liu, Xue Liu, “Gradient-based Bi-level Optimization for Deep Learning: A Survey”, Jul 2022.
[Paper]

[2] Can Chen, Chen Ma, Xi Chen, Sirui Song, Hao Liu, Xue Liu, “Unbiased Implicit Feedback via Bi-level Optimization”, May 2022.
[Paper]

[1] Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu, “Multi-FR: A Multi-Objective Optimization Method for Achieving Two-sided Fairness in E-commerce Recommendation”, May 2021.
[Paper]

Thesis

  • “Towards Effective Recommendation: Neural Networks and Adaptive Learning”, Jun 2021 [Paper].
    • Committee: Prof. Xue Liu (Advisor), Prof. Mark Coates, Prof. Xiao-Wen Chang, Prof. Benjamin Fung, and Prof. Jian Pei