10 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…
OneRank: Unified Transformer-Native Ranking Architecture for Multi-Task Recommendation
Jiakai Tang, Sunhao Dai, Kun Wang +8
Multi-task learning (MTL) is essential in recommender systems to enable complementary learning among diverse user feedback. While modern industrial practices have shifted from DNNs…
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…
Reveal Hidden Pitfalls and Navigate Next Generation of Vector Similarity Search from Task-Centric Views
Tingyang Chen, Cong Fu, Jiahua Wu +6
Vector Similarity Search (VSS) in high-dimensional spaces is rapidly emerging as core functionality in next-generation database systems for numerous data-intensive services -- from…
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…