8 papers
FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models
Song-Li Wu, Xianquan Wang, Zhaocheng Du +2
While the "Matthew Effect" and filter bubbles are widely recognized outcome-level biases in recommender systems, we reveal that Diffusion Recommender Models (DRMs) uniquely compoun…
FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation
Song-Li Wu, Weinan Gan, Zhaocheng Du +2
A critical prerequisite of generative recommendation is designing semantic identifiers (SIDs) that are both scalable to large item sets and efficiently learnable. Existing SID lear…
ActiveMem: Dynamic Latent Memory Trees for Long-Horizon Agents
Song-Li Wu, Jingyi Wang, Zhaocheng Du +1
Large Language Model (LLM) agents increasingly rely on external memory to support long-horizon reasoning and decision making. Existing memory systems typically retrieve historical…
CodeSkill: Latent Skill Abstraction for Long-Horizon Code Agents
Song-Li Wu, Jingyi Wang, Zhaocheng Du +2
Code agents require long-horizon decision-making over complex interaction trajectories. However, existing reinforcement learning (RL) approaches typically optimize behavior at the…
When Hard Negatives Hurt: Bridging the Generative-Discriminative Gap in Hard Negative Synthesis for Retrieval
Zhicheng Zhang, Jiwei Tang, Kuicai Dong +9
Hard negative mining has become the dominant strategy for training retrievers, yet it faces intrinsic limitations: negatives are bounded by corpus availability, selected by retriev…
MALLOC: Benchmarking the Memory-aware Long Sequence Compression for Large Sequential Recommendation
Qihang Yu, Kairui Fu, Zhaocheng Du +10
The scaling law, which indicates that model performance improves with increasing dataset and model capacity, has fueled a growing trend in expanding recommendation models in both i…
Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction
Zhicheng Zhang, Zhaocheng Du, Jieming Zhu +8
User behavior sequences in modern recommendation systems exhibit significant length heterogeneity, ranging from sparse short-term interactions to rich long-term histories. While lo…
CollectiveKV: Decoupling and Sharing Collaborative Information in Sequential Recommendation
Jingyu Li, Zhaocheng Du, Qianhui Zhu +5
Sequential recommendation models are widely used in applications, yet they face stringent latency requirements. Mainstream models leverage the Transformer attention mechanism to im…