9 papers
Revealing After Overwriting: An Exponential POMDP OPE Lower Bound under History-Dependent Logging
Youyu Luo, Pengzhan Zhou, Zhida Qin +4
Multi-step revealing can make off-policy evaluation tractable under memoryless logging. With history-dependent logging, state decodability and target-relevant evidence can separate…
Bridging Behavior and Semantics for Time-aware Cross-Domain Sequential Recommendation
Zhida Qin, Zemu Liu, Haoyan Fu +4
Cross-domain sequential recommendation (CDSR) alleviates interaction sparsity by jointly modeling user behaviors across multiple domains. While current studies have made some progr…
AutoUE: Automated Generation of 3D Games in Unreal Engine via Multi-Agent Systems
Lei Yin, Wentao Cheng, Zhida Qin +3
Automatically generating 3D games in commercial game engines remains a non-trivial challenge, as it involves complex engine-related workflows for generating assets such as scenes,…
InfoDCL: Informative Noise Enhanced Diffusion Based Contrastive Learning
Xufeng Liang, Zhida Qin, Chong Zhang +2
Contrastive learning has demonstrated promising potential in recommender systems. Existing methods typically construct sparser views by randomly perturbing the original interaction…
Time Matters: Enhancing Sequential Recommendations with Time-Guided Graph Neural ODEs
Haoyan Fu, Zhida Qin, Shixiao Yang +5
Sequential recommendation (SR) is widely deployed in e-commerce platforms, streaming services, etc., revealing significant potential to enhance user experience. However, existing m…
From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection
Lincan Cai, Jingxuan Kang, Shuang Li +4
Pretrained vision-language models (VLMs), e.g., CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmen…
Large Language Models Enhanced Hyperbolic Space Recommender Systems
Wentao Cheng, Zhida Qin, Zexue Wu +2
Large Language Models (LLMs) have attracted significant attention in recommender systems for their excellent world knowledge capabilities. However, existing methods that rely on Eu…
FedAH: Aggregated Head for Personalized Federated Learning
Pengzhan Zhou, Yuepeng He, Yijun Zhai +5
Recently, Federated Learning (FL) has gained popularity for its privacy-preserving and collaborative learning capabilities. Personalized Federated Learning (PFL), building upon FL,…
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
Yijun Zhai, Pengzhan Zhou, Yuepeng He +5
The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great pote…