6 papers
Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation
Zixuan Wang, Yuhong Chen, Yuxuan Zhu +10
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and h…
ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation
Kun Yang, Yuxuan Zhu, Yazhe Chen +7
Sequential recommendation increasingly employs latent multi-step reasoning to enhance test-time computation. Despite empirical gains, existing approaches largely drive intermediate…
Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs
Yu Liang, Zhongjin Zhang, Yuxuan Zhu +10
Semantic ID (SID)-based recommendation is a promising paradigm for scaling sequential recommender systems, but existing methods largely follow a semantic-centric pipeline: item emb…
A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems
Yuxuan Zhu, Cong Fu, Yabo Ni +2
Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to ca…
OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Sunhao Dai, Jiakai Tang, Jiahua Wu +13
Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain…
Embed Progressive Implicit Preference in Unified Space for Deep Collaborative Filtering
Zhongjin Zhang, Yu Liang, Cong Fu +5
Embedding-based collaborative filtering, often coupled with nearest neighbor search, is widely deployed in large-scale recommender systems for personalized content selection. Moder…