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2020 – today
- 2026
[j8]Sijin Lu, Fangyuan Luo, Jun Wu:
Counterfactual reasoning for alleviating dual biases in recommendation. Int. J. Mach. Learn. Cybern. 17(4): 150 (2026)- [c10]Jundong Chen
, Honglei Zhang, Chunxu Zhang, Fangyuan Luo, Yidong Li:
Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach. AAAI 2026: 14547-14555 - 2025
- [j7]Fangyuan Luo, Xin Li:
Higher-degrees Hybrid Non-uniform Subdivision Surfaces. Comput. Aided Des. 179: 103822 (2025) - [j6]Fangyuan Luo, Yankai Chen, Jun Wu, Yidong Li:
Rank Gap Sensitive Deep AUC maximization for CTR prediction. Pattern Recognit. 164: 111496 (2025) - [c9]Sijin Lu, Fangyuan Luo, Jun Wu:
Alleviating Dual Biases in Recommendation (Student Abstract). AAAI 2025: 29421-29422 - [c8]Wenwei Kou, Jinlian Du, Hang Su, Fangyuan Luo:
The Research on GPU-Based Relational Join Algorithms. HP3C 2025: 114-122 - [c7]Sijin Lu, Zhibo Man, Fangyuan Luo, Jun Wu:
Dual Debiasing in LLM-based Recommendation. SIGIR 2025: 2685-2689 - [i3]Jundong Chen, Honglei Zhang, Chunxu Zhang, Fangyuan Luo, Yidong Li:
Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach. CoRR abs/2508.12386 (2025) - 2024
- [j5]Zhiyuan Li, Fangyuan Luo:
Application and value of Internet of Things in enterprise supply chain management. J. Comput. Methods Sci. Eng. 24(4-5): 2689-2703 (2024) - [j4]Fangyuan Luo, Jun Wu, Tao Wang:
Discrete Listwise Content-aware Recommendation. ACM Trans. Knowl. Discov. Data 18(1): 7:1-7:20 (2024) - [j3]Xiangkui Lu, Jun Wu, Junheng Huang, Fangyuan Luo, Jianbo Yuan:
Co-Training-Teaching: A Robust Semi-Supervised Framework for Review-Aware Rating Regression. ACM Trans. Knowl. Discov. Data 18(2): 41:1-41:16 (2024) - [c6]Fangyuan Luo, Jun Wu:
Optimizing Recall in Deep Graph Hashing Framework for Item Retrieval (Student Abstract). AAAI 2024: 23569-23570 - [d1]Fangyuan Luo:
HashRec-Datasets. IEEE DataPort, 2024
- [i2]Fangyuan Luo, Honglei Zhang, Tong Li, Jun Wu:
Learning to Hash for Recommendation: A Survey. CoRR abs/2412.03875 (2024) - 2023
- [j2]Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, Yidong Li:
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization. ACM Trans. Inf. Syst. 41(4): 90:1-90:28 (2023) - [c5]Fangyuan Luo, Jun Wu:
User-Dependent Learning to Debias for Recommendation. SIGIR 2023: 2491-2495 - 2022
- [c4]Fangyuan Luo, Jun Wu, Tao Wang:
Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback. IJCAI 2022: 2159-2165 - [c3]Shuang Tang, Fangyuan Luo, Jun Wu:
Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction. SIGIR 2022: 2400-2404 - [i1]Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, Yidong Li:
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization. CoRR abs/2206.11743 (2022) - 2021
- [c2]Fangyuan Luo, Jun Wu, Haishuai Wang:
Semi-Discrete Social Recommendation (Student Abstract). AAAI 2021: 15835-15836 - [c1]Junheng Huang, Fangyuan Luo, Jun Wu:
Semi-supervised Factorization Machines for Review-Aware Recommendation. DASFAA (3) 2021: 85-99 - 2020
- [j1]Jun Wu, Fangyuan Luo, Yujia Zhang, Haishuai Wang:
Semi-discrete Matrix Factorization. IEEE Intell. Syst. 35(5): 73-83 (2020)
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