Yongxiang Tang

19 papers A* 3A 3Journal 11Unranked 2
YearRankTypeTitle / Venue / Authors
2026 A* conf
Fairness-Aware Design for Contextual Experiments: Guaranteeing Reliability and Equity in Heterogeneous Subgroups.
AAAI
Guangyan Gan, Ling Zhang, Yanhua Cheng, Yongxiang Tang, Kaiyuan Li, Xialong Liu, Peng Jiang
2025 J jnl
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation.
CoRR
Kaiyuan Li, Rui Xiang, Yong Bai, Yongxiang Tang, Yanhua Cheng, Xialong Liu, Peng Jiang, Kun Gai
2025 J jnl
CHIME: A Compressive Framework for Holistic Interest Modeling.
CoRR
Yong Bai, Rui Xiang, Kaiyuan Li, Yongxiang Tang, Yanhua Cheng, Xialong Liu, Peng Jiang, Kun Gai
2025 A conf
Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion.
CIKM
Moyu Zhang, Yongxiang Tang, Yujun Jin, Jinxin Hu, Yu Zhang
2025 J jnl
Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion.
CoRR
Moyu Zhang, Yongxiang Tang, Yujun Jin, Jinxin Hu, Yu Zhang
2025 J jnl
INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems.
CoRR
Kaiyuan Li, Dongdong Mao, Yongxiang Tang, Yanhua Cheng, Yanxiang Zeng, Chao Wang, Xialong Liu, Peng Jiang
2025 A* conf
Learning Monotonic Probabilities with a Generative Cost Model.
ICML
Yongxiang Tang, Yanhua Cheng, Xiaocheng Liu, Jiaochen Chen, Yanxiang Zeng, Ning Luo, Pengjia Yuan, Xialong Liu, Peng Jiang
2025 J jnl
Learning Monotonic Probabilities with a Generative Cost Model.
CoRR
Yongxiang Tang, Yanhua Cheng, Xiaocheng Liu, Chenchen Jiao, Yanxiang Zeng, Ning Luo, Pengjia Yuan, Xialong Liu, Peng Jiang
2025 — conf
Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement.
WWW (Companion Volume)
Hao Jiang, Yongxiang Tang, Yanxiang Zeng, Pengjia Yuan, Yanhua Cheng, Teng Sha, Xialong Liu, Peng Jiang
2025 J jnl
Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement.
CoRR
Hao Jiang, Yongxiang Tang, Yanxiang Zeng, Pengjia Yuan, Yanhua Cheng, Teng Sha, Xialong Liu, Peng Jiang
2025 — conf
Reward Balancing Revisited: Enhancing Offline Reinforcement Learning for Recommender Systems.
WWW (Companion Volume)
Wenzheng Shu, Yanxiang Zeng, Yongxiang Tang, Teng Sha, Ning Luo, Yanhua Cheng, Xialong Liu, Fan Zhou, Peng Jiang
2025 J jnl
Reward Balancing Revisited: Enhancing Offline Reinforcement Learning for Recommender Systems.
CoRR
Wenzheng Shu, Yanxiang Zeng, Yongxiang Tang, Teng Sha, Ning Luo, Yanhua Cheng, Xialong Liu, Fan Zhou, Peng Jiang
2025 A conf
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain.
WSDM
Rui Xia, Yanhua Cheng, Yongxiang Tang, Xiaocheng Liu, Xialong Liu, Lisong Wang, Peng Jiang
2025 J jnl
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain.
CoRR
Rui Xia, Yanhua Cheng, Yongxiang Tang, Xiaocheng Liu, Xialong Liu, Lisong Wang, Peng Jiang
2025 J jnl
VQL: An End-to-End Context-Aware Vector Quantization Attention for Ultra-Long User Behavior Modeling.
CoRR
Kaiyuan Li, Yongxiang Tang, Yanhua Cheng, Yong Bai, Yanxiang Zeng, Chao Wang, Xialong Liu, Peng Jiang
2024 A* conf
Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context.
SIGIR
Moyu Zhang, Yongxiang Tang, Jinxin Hu, Yu Zhang
2024 J jnl
Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context.
CoRR
Moyu Zhang, Yongxiang Tang, Jinxin Hu, Yu Zhang
2022 A conf
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems.
CIKM
Yongxiang Tang, Wentao Bai, Guilin Li, Xialong Liu, Yu Zhang
2022 J jnl
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems.
CoRR
Yongxiang Tang, Wentao Bai, Guilin Li, Xialong Liu, Yu Zhang