6 papers · 1 filter
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…
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…
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…
REG4Rec: Reasoning-Enhanced Generative Model for Large-Scale Recommendation Systems
Haibo Xing, Hao Deng, Yucheng Mao +9
Sequential recommendation aims to predict a user's next action in large-scale recommender systems. While traditional methods often suffer from insufficient information interaction,…