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How Powerful are Interest Diffusion on Purchasing Prediction: A Case Study of Taocode
Published in SIGIR, 2021
A dynamic graph neural network for modeling product-interest diffusion through Taocode sharing and predicting purchases.
Recommended citation: Xuanwen Huang, Yang Yang, Ziqiang Cheng, Shen Fan, Zhongyao Wang, Juren Li, Jun Zhang, and Jingmin Chen. How Powerful are Interest Diffusion on Purchasing Prediction: A Case Study of Taocode. SIGIR 2021.
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DWLR: Domain Adaptation under Label Shift for Wearable Sensor
Published in IJCAI, 2024
A learnable reweighting framework for handling both label shift and feature shift in wearable-sensor domain adaptation.
Recommended citation: Juren Li, Yang Yang, Youmin Chen, Jianfeng Zhang, Zeyu Lai, and Lujia Pan. DWLR: Domain Adaptation under Label Shift for Wearable Sensor. IJCAI 2024.
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Disentangling Domain and General Representations for Time Series Classification
Published in IJCAI, 2024
A disentangled representation-learning framework for separating transferable and domain-specific patterns in time series.
Recommended citation: Youmin Chen, Xinyu Yan, Yang Yang, Jianfeng Zhang, Jing Zhang, Lujia Pan, and Juren Li. Disentangling Domain and General Representations for Time Series Classification. IJCAI 2024.
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Chromosomal Structural Abnormality Diagnosis by Homologous Similarity
Published in KDD, 2024
A homologous-similarity framework for detecting structural chromosome abnormalities from paired chromosome images.
Recommended citation: Juren Li, Fanzhe Fu, Ran Wei, Yifei Sun, Zeyu Lai, Ning Song, Xin Chen, and Yang Yang. Chromosomal Structural Abnormality Diagnosis by Homologous Similarity. KDD 2024.
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LiPM: Foundation Model for Lithium-Ion Battery Analysis
Published in KDD, 2025
A physics-aware foundation model for learning transferable representations from heterogeneous, irregularly sampled battery data.
Recommended citation: Juren Li, Yang Yang, Hanchen Su, Jiayu Liu, Youmin Chen, Jianfeng Zhang, and Lujia Pan. LiPM: Foundation Model for Lithium-Ion Battery Analysis. KDD 2025.
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Rec-Distill: An Industrial Distillation Pipeline for Large-Scale Recommendation Models
Published in arXiv preprint, 2026
An industrial distillation pipeline for transferring gains from large-scale recommendation models to efficient serving models.
Recommended citation: Haoran Ding, Wenlin Zhao, Yuchen Jiang, Juren Li, et al. (2026). Rec-Distill: An Industrial Distillation Pipeline for Large-Scale Recommendation Models. arXiv:2605.29755.
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