Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model
<|reference_start|>Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model: In large e-commerce platforms, search systems are typically composed of a series of modules, including recall, pre-ranking, and ranking phases. The pre-ranking phase, serving as a lightweight module, is crucial for filtering out the bulk of products in advance for the downstream ranking module. Industrial efforts on optimizing the pre-ranking model have predominantly focused on enhancing ranking consistency, model structure, and generalization towards long-tail items. Beyond these optimizations, meeting the system performance requirements presents a significant challenge. Contrasting with existing industry works, we propose a novel method: a Generalizable and RAnk-ConsistEnt Pre-Ranking Model (GRACE), which achieves: 1) Ranking consistency by introducing multiple binary classification tasks that predict whether a product is within the top-k results as estimated by the ranking model, which facilitates the addition of learning objectives on common point-wise ranking models; 2) Generalizability through contrastive learning of representation for all products by pre-training on a subset of ranking product embeddings; 3) Ease of implementation in feature construction and online deployment. Our extensive experiments demonstrate significant improvements in both offline metrics and online A/B test: a 0.75% increase in AUC and a 1.28% increase in CVR.<|reference_end|>
@article{xu2024optimizing,
    title={Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent
  Pre-Ranking Model},
    author={Enqiang Xu, Yiming Qiu, Junyang Bai, Ping Zhang, Dadong Miao, Songlin
  Wang, Guoyu Tang, Lin Liu, Mingming Li},
    journal={arXiv preprint arXiv:2405.05606},
    year={2024},
    doi={10.1145/3626772.3661343},
    archivePrefix={arXiv},
    eprint={2405.05606},
    primaryClass={cs.IR cs.LG}
}