The problem of search relevance in the E-commerce domain is a challenging one
since it involves understanding the intent of a user's short nuanced query and
matching it with the appropriate products in the catalog. This problem has
traditionally been addressed using language models (LMs) and graph neural
networks (GNNs) to capture semantic and inter-product behavior signals,
respectively. However, the rapid development of new architectures has created a
gap between research and the practical adoption of these techniques. Evaluating
the generalizability of these models for deployment requires extensive
experimentation on complex, real-world datasets, which can be non-trivial and
expensive. Furthermore, such models often operate on latent space
representations that are incomprehensible to humans, making it difficult to
evaluate and compare the effectiveness of different models. This lack of
interpretability hinders the development and adoption of new techniques in the
field. To bridge this gap, we propose Plug and Play Graph LAnguage Model
(PP-GLAM), an explainable ensemble of plug and play models. Our approach uses a
modular framework with uniform data processing pipelines. It employs additive
explanation metrics to independently decide whether to include (i) language
model candidates, (ii) GNN model candidates, and (iii) inter-product behavioral
signals. For the task of search relevance, we show that PP-GLAM outperforms
several state-of-the-art baselines as well as a proprietary model on real-world
multilingual, multi-regional e-commerce datasets. To promote better model
comprehensibility and adoption, we also provide an analysis of the
explainability and computational complexity of our model. We also provide the
public codebase and provide a deployment strategy for practical implementation.

在电子商务领域中，搜索相关性问题具有挑战性，因为它涉及理解用户的简短微妙查询的意图，并将其与目录中的适当产品匹配。为了弥合研究和实际采用这些技术之间的差距，我们提出了 Plug and Play Graph LAnguage Model（PP-GLAM），一个可解释的插拔模型集合。通过均匀的数据处理流程和增加的解释度量，我们展示了 PP-GLAM 在真实多语言、多区域电子商务数据集上优于几种最先进的基准模型及专有模型。为了促进更好的模型可理解性和采用度，我们还提供了模型的解释性和计算复杂性分析，并提供了实际实施的部署策略的公共代码库。

面向电子商务的可解释图模型和语言模型集成提升搜索相关性

An Interpretable Ensemble of Graph and Language Models for Improving  Search Relevance in E-Commerce

Embedding-based neural retrieval is a prevalent approach to address the
semantic gap problem which often arises in product search on tail queries. In
contrast, popular queries typically lack context and have a broad intent where
additional context from users historical interaction can be helpful. In this
paper, we share our novel approach to address both: the semantic gap problem
followed by an end to end trained model for personalized semantic retrieval. We
propose learning a unified embedding model incorporating graph, transformer and
term-based embeddings end to end and share our design choices for optimal
tradeoff between performance and efficiency. We share our learnings in feature
engineering, hard negative sampling strategy, and application of transformer
model, including a novel pre-training strategy and other tricks for improving
search relevance and deploying such a model at industry scale. Our personalized
retrieval model significantly improves the overall search experience, as
measured by a 5.58% increase in search purchase rate and a 2.63% increase in
site-wide conversion rate, aggregated across multiple A/B tests - on live
traffic.

本文提出了一种能够同时解决语义差异问题和根据用户历史交互提供个性化语义搜索结果的新方法，将图嵌入、transformer 模型和基于术语的嵌入统一到一个嵌入模型中，同时分享了特征工程、硬负采样策略以及 transformer 模型应用方面的技巧。此个性化检索模型增强了用户的搜索体验，并提高了搜索购买率和网站整体转化率。