7 papers
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…
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…
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum Inner Product Search
Tingyang Chen, Cong Fu, Xiangyu Ke +3
Maximum Inner Product Search (MIPS) is a fundamental challenge in machine learning and information retrieval, particularly in high-dimensional data applications. Existing approache…