Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model
arxiv(2024)
摘要
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 CVR.
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