Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model

Beyond these optimizations, meeting the system To enhance user experience and conversion efficiency, the online performance requirements presents a significant challenge. Contrasting search system is employed with a cascading architecture, mainly with existing industry works, we propose a novel method: a including recall and ranking. The ranking stage as the downstream Generalizable and RAnk-ConsistEnt Pre-Ranking Model (GRACE), component directly influences the efficiency of item sorting. Several which achieves: 1) Ranking consistency by introducing multiple superior ranking models have been identified in industrial research, binary classification tasks that predict whether a product is within such as MMoE [4], PLE [12], ESMM [5], DeepFM [1], DIN [18], the top-k results as estimated by the ranking model, which facilitates MIMN [8], SDIM [16], and SIM [12], with a focus on feature engineering, the addition of learning objectives on common point-wise behavioral sequence modeling, and objective function ranking models; 2) Generalizability through contrastive learning optimization. However, as the scale of products within the search of representation for all products by pre-training on a subset of system grows, there is an increasing demand for managing the ranking product embeddings; 3) Ease of implementation in feature time complexity of the sorting module.