Quanyu Dai

1.5k total citations
44 papers, 829 citations indexed

About

Quanyu Dai is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Quanyu Dai has authored 44 papers receiving a total of 829 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 22 papers in Information Systems and 12 papers in Management Science and Operations Research. Recurrent topics in Quanyu Dai's work include Recommender Systems and Techniques (22 papers), Advanced Graph Neural Networks (18 papers) and Domain Adaptation and Few-Shot Learning (10 papers). Quanyu Dai is often cited by papers focused on Recommender Systems and Techniques (22 papers), Advanced Graph Neural Networks (18 papers) and Domain Adaptation and Few-Shot Learning (10 papers). Quanyu Dai collaborates with scholars based in China, Hong Kong and Sweden. Quanyu Dai's co-authors include Xiao Shen, Zhenhua Dong, Dan Wang, Dan Wang, Qiang Li, Jian Tang, Fu-Lai Chung, Jieming Zhu, Kup‐Sze Choi and Rui Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Pattern Recognition and Information Sciences.

In The Last Decade

Quanyu Dai

39 papers receiving 814 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Quanyu Dai China 16 622 296 163 141 111 44 829
Chanyoung Park South Korea 17 538 0.9× 361 1.2× 147 0.9× 142 1.0× 88 0.8× 52 781
Jianxin Ma China 10 448 0.7× 331 1.1× 127 0.8× 97 0.7× 82 0.7× 15 599
Yue Hu China 16 599 1.0× 225 0.8× 285 1.7× 62 0.4× 59 0.5× 69 811
Fei Cai China 16 543 0.9× 463 1.6× 116 0.7× 67 0.5× 106 1.0× 55 738
Shaohua Fan China 8 571 0.9× 247 0.8× 138 0.8× 178 1.3× 26 0.2× 14 714
Mathias Niepert United States 16 801 1.3× 204 0.7× 204 1.3× 44 0.3× 182 1.6× 60 970
X. H. Xie China 1 431 0.7× 330 1.1× 124 0.8× 41 0.3× 33 0.3× 2 594

Countries citing papers authored by Quanyu Dai

Since Specialization
Citations

This map shows the geographic impact of Quanyu Dai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Quanyu Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quanyu Dai more than expected).

Fields of papers citing papers by Quanyu Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Quanyu Dai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Quanyu Dai. The network helps show where Quanyu Dai may publish in the future.

Co-authorship network of co-authors of Quanyu Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Quanyu Dai. A scholar is included among the top collaborators of Quanyu Dai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Quanyu Dai. Quanyu Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dai, Quanyu, et al.. (2025). MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising. ArXiv.org. 4408–4419. 1 indexed citations
2.
3.
Dai, Quanyu, et al.. (2025). MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents. 821–824. 1 indexed citations
4.
Zhu, Jieming, Yanting Yang, Quanyu Dai, et al.. (2024). Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey. 6566–6576. 13 indexed citations
5.
Dai, Sunhao, Jieming Zhu, Zhenhua Dong, et al.. (2024). Modeling User Attention in Music Recommendation. 761–774. 2 indexed citations
6.
Du, Zhaocheng, Jieming Zhu, D. Zou, et al.. (2024). UniEmbedding: Learning Universal Multi-Modal Multi-Domain Item Embeddings via User-View Contrastive Learning. 4446–4453. 1 indexed citations
7.
Zhu, Jieming, et al.. (2024). Benchmarking News Recommendation in the Era of Green AI. 971–974. 4 indexed citations
8.
Dai, Quanyu, Xiao Shen, Xiaochen Xie, et al.. (2024). Semi-supervised domain adaptation on graphs with contrastive learning and minimax entropy. Neurocomputing. 580. 127469–127469. 5 indexed citations
9.
Zhang, Jingsen, et al.. (2024). Active Explainable Recommendation with Limited Labeling Budgets. 5375–5379.
10.
Chen, Xu, et al.. (2024). Would You Like Your Data to Be Trained? A User Controllable Recommendation Framework. Proceedings of the AAAI Conference on Artificial Intelligence. 38(19). 21673–21680.
11.
Chen, Xu, et al.. (2024). Reflective Multi-Agent Collaboration based on Large Language Models. 138595–138631. 1 indexed citations
12.
Zhu, Jieming, Guohao Cai, Quanyu Dai, et al.. (2023). FINAL: Factorized Interaction Layer for CTR Prediction. 2006–2010. 16 indexed citations
13.
Wu, Chuhan, et al.. (2023). Task Adaptive Multi-learner Network for Joint CTR and CVR Estimation. 490–494. 1 indexed citations
14.
Zhang, Xiao, Sunhao Dai, Jun Xu, et al.. (2022). Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2504–2514. 15 indexed citations
15.
Dai, Quanyu, et al.. (2022). Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution. IEEE Transactions on Knowledge and Data Engineering. 1–1. 64 indexed citations
16.
Zhu, Jieming, Quanyu Dai, Jinyang Liu, et al.. (2022). BARS. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2912–2923. 41 indexed citations
17.
Wu, Peng, Wenjie Hu, Quanyu Dai, et al.. (2022). On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 5646–5653. 22 indexed citations
18.
Shen, Xiao, et al.. (2020). Network Together: Node Classification via Cross-Network Deep Network Embedding. IEEE Transactions on Neural Networks and Learning Systems. 32(5). 1935–1948. 68 indexed citations
19.
Lei, Yu, Zhitao Wang, Wenjie Li, Hongbin Pei, & Quanyu Dai. (2020). Social Attentive Deep Q-Networks for Recommender Systems. IEEE Transactions on Knowledge and Data Engineering. 34(5). 2443–2457. 15 indexed citations
20.
Zheng, Zimu, Yuqi Wang, Quanyu Dai, Huadi Zheng, & Dan Wang. (2019). Metadata-driven Task Relation Discovery for Multi-task Learning. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4426–4432. 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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