• Corpus ID: 54446463

EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search

@article{Wu2018EENMFAE,
  title={EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search},
  author={Wenjin Wu and Guojun Liu and Hui Ye and Chenshuang Zhang and Tianshu Wu and Daorui Xiao and Wei Lin and Xiaoyu Zhu},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.01190}
}
E-commerce sponsored search contributes an important part of revenue for the e-commerce company. In consideration of effectiveness and efficiency, a large-scale sponsored search system commonly adopts a multi-stage architecture. We name these stages as ad retrieval, ad pre-ranking and ad ranking. Ad retrieval and ad pre-ranking are collectively referred to as ad matching in this paper. We propose an end-to-end neural matching framework (EENMF) to model two tasks---vector-based ad retrieval and… 

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