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LORE: A Large Generative Model for Search Relevance
Chenji Lu, Zhuo Chen, Hui Zhao +9
Achievement. We introduce LORE, a systematic framework for Large Generative Model-based relevance in e-commerce search. Deployed and iterated over three years, LORE achieves a cumu…
RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment
Chenji Lu, Zhuo Chen, Hui Zhao +4
Search relevance plays a central role in web e-commerce. While large language models (LLMs) have shown significant results on relevance task, existing benchmarks lack sufficient co…
MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling
Bencheng Yan, Si Chen, Shichang Jia +12
Click-Through Rate (CTR) prediction is a crucial task in recommendation systems, online searches, and advertising platforms, where accurately capturing users' real interests in con…
Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning
Gang Zhao, Ximing Zhang, Chenji Lu +5
Effective query-item relevance modeling is pivotal for enhancing user experience and safeguarding user satisfaction in e-commerce search systems. Recently, benefiting from the vast…