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20232026
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cs.IR2026

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…

cs.IR2026

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…

cs.IR2026

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…

cs.IR2025

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…

cs.IR2025

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…

cs.IR2025

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…

cs.IR2024

Residual Multi-Task Learner for Applied Ranking

Cong Fu, Kun Wang, Jiahua Wu +5

Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their…