5 citations · 12 across the 21 of their papers we have counts for
12 papers · 1 filter
Cross-Country Code-Mixing for Generative Recommendation
Yuan Gao, Hao Deng, Haibo Xing +5
Cross-country recommendation on modern e-commerce platforms is typically deployed with disjoint user and item ID spaces across markets, removing the shared anchors that conventiona…
Astar: Learning to Propose Evolution Directions for Self-Evolving Industrial AI Systems
Jinxin Hu, Hao Deng, Haibo Xing +12
Modern AI systems advance through continuous iteration: a loop of proposing evolution directions, implementing code, training, and evaluation. While the latter three stages are inc…
SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation
Yu Cui, Yi Xu, Jiahao Wang +6
Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism to recommendation often yields…
EvoRec: Self Evolving Agentic Recommender Systems
Lingyu Mu, Hao Deng, Haibo Xing +3
Optimizing modern recommender systems still relies heavily on engineers iterating by hand, which is slow and bounded by individual expertise. LLM-based agents open a path toward au…
LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation
Lingyu Mu, Hao Deng, Haibo Xing +7
Recent progress in large language model (LLM) based generative recommendation (GR) shows that leveraging LLM world knowledge can substantially improve performance. However, existin…
Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
Jinxin Hu, Hao Deng, Lingyu Mu +4
Large-scale industrial recommenders typically use a fixed multi-stage pipeline (recall, ranking, re-ranking) and have progressed from collaborative filtering to deep and large pre-…
AgenticRS-Architecture: System Design for Agentic Recommender Systems
Hao Zhang, Jinxin Hu, Hao Deng +4
AutoModel is an agent based architecture for the full lifecycle of industrial recommender systems. Instead of a fixed recall and ranking pipeline, AutoModel organizes recommendatio…
RCLRec: Reverse Curriculum Learning for Modeling Sparse Conversions in Generative Recommendation
Yulei Huang, Hao Deng, Haibo Xing +5
Conversion objectives in large-scale recommender systems are sparse, making them difficult to optimize. Generative recommendation (GR) partially alleviates data sparsity by organiz…
Deferred is Better: A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Features
Yi Xu, Moyu Zhang, Chaofan Fan +3
Click-through rate (CTR) prediction models estimates the probability of a user-item click by modeling interactions across a vast feature space. A fundamental yet often overlooked c…
Bridging Sequential and Contextual Features with a Dual-View of Fine-grained Core-Behaviors and Global Interest-Distribution
Yi Xu, Chaofan Fan, Moyu Zhang +6
Click-through rate (CTR) prediction tasks typically estimate the probability of a user clicking on a candidate item by modeling both user behavior sequence features and the item's…
Learning to Reflect and Correct: Towards Better Decoding Trajectories for Large-Scale Generative Recommendation
Haibo Xing, Hao Deng, Lingyu Mu +4
Generative Recommendation (GR) has become a promising paradigm for large-scale recommendation systems. However, existing GR models typically perform single-pass decoding without ex…
Masked Diffusion Generative Recommendation
Lingyu Mu, Hao Deng, Haibo Xing +4
Generative recommendation (GR) typically first quantizes continuous item embeddings into multi-level semantic IDs (SIDs), and then generates the next item via autoregressive decodi…