Result relevance prediction is an essential task of e-commerce search engines to boost the utility of search engines and ensure smooth user experience. The last few years eyewitnessed a flurry of research on the use of Transformer-style models and deep text-match models to improve relevance. However, these two types of models ignored the inherent bipartite network structures that are ubiquitous in e-commerce search logs, making these models ineffective. We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction. We design, for the first time, an end-to-end First-and-Second-order Relevance prediction model for e-commerce item relevance. The model is augmented by the neighborhood structures of bipartite networks that are built using the information of user behavioral feedback, including clicks and purchases. To ensure that edges accurately encode relevance information, we introduce external knowledge generated from BERT to refine the network of user behaviors. This allows the new model to integrate information from neighboring items and queries, which are highly relevant to the focus query-item pair under consideration. Results of offline experiments showed that the new model significantly improved the prediction accuracy in terms of human relevance judgment. An ablation study showed that the First-and-Second-order model gained a 4.3% average gain over the First-order model. Results of an online A/B test revealed that the new model derived more commercial benefits compared to the base model.

中文翻译：

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电子商务搜索中深度语义相关性匹配的异构网络嵌入

结果相关性预测是电子商务搜索引擎的一项基本任务，可以提高搜索引擎的效用并确保流畅的用户体验。最近几年，目睹了关于使用Transformer样式模型和深层文本匹配模型来提高相关性的大量研究。但是，这两种模型忽略了电子商务搜索日志中普遍存在的固有的双向网络结构，从而使这些模型无效。我们在本文中提出了一种新颖的二阶相关性，以改善结果相关性预测，该二阶相关性与先前的一阶相关性根本不同。我们首次设计了用于电子商务项目相关性的端到端一阶和二阶相关性预测模型。通过使用用户行为反馈信息（包括点击和购买）构建的双向网络的邻域结构来增强该模型。为了确保边缘准确地编码相关性信息，我们引入了从BERT生成的外部知识，以完善用户行为网络。这允许新模型集成来自相邻项目和查询的信息，这些信息与所考虑的焦点查询-项目对高度相关。离线实验的结果表明，该新模型在人类相关性判断方面显着提高了预测准确性。消融研究表明，一阶和二阶模型比一阶模型获得了4.3％的平均增益。