5 papers
GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
Yanyan Zou, Junbo Qi, Lunsong Huang +7
Generative Retrieval (GR) offers a promising paradigm for recommendation through next-token prediction (NTP). However, scaling it to large-scale industrial systems introduces three…
Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer
Huimu Wang, Xingzhi Yao, Yiming Qiu +6
While semantic ID-based generative retrieval enables efficient end-to-end modeling in industrial applications, these methods face a persistent trade-off. On one hand, data-rich hea…
RAD-DPO: Robust Adaptive Denoising Direct Preference Optimization for Generative Retrieval in E-commerce
Zhiguo Chen, Guohao Sun, Yiming Qiu +6
Generative Retrieval (GR) is rapidly transforming e-commerce search by replacing traditional multi-stage pipelines with the autoregressive decoding of structured Semantic IDs (SIDs…
A Simple and Effective Framework for Symmetric Consistent Indexing in Large-Scale Dense Retrieval
Huimu Wang, Yiming Qiu, Xingzhi Yao +5
Dense retrieval has become the industry standard in large-scale information retrieval systems due to its high efficiency and competitive accuracy. Its core relies on a coarse-to-fi…
LREF: A Novel LLM-based Relevance Framework for E-commerce
Tian Tang, Zhixing Tian, Zhenyu Zhu +5
Query and product relevance prediction is a critical component for ensuring a smooth user experience in e-commerce search. Traditional studies mainly focus on BERT-based models to…