This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to users' search queries. Ambiguity and complexity of user queries often lead to a mismatch between the user's intent and retrieved product titles or documents. Recent approaches have proposed the use of Transformer-based models, which need millions of annotated query-title pairs during the pre-training stage, and this data often does not take user intent into account. To tackle this, we curate samples from existing datasets at eBay, manually annotated with buyer-centric relevance scores and centrality scores, which reflect how well the product title matches the users' intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimises for the user intent in semantic product search. To that end, we propose a dual-loss based optimisation to handle hard negatives, i.e., product titles that are semantically relevant but do not reflect the user's intent. Our contributions include curating challenging evaluation sets and implementing UCO, resulting in significant product ranking efficiency improvements observed for different evaluation metrics. Our work aims to ensure that the most buyer-centric titles for a query are ranked higher, thereby, enhancing the user experience on e-commerce platforms.

翻译：本文旨在通过提升与用户搜索查询相关的商品排序质量来改善电子商务平台的用户体验。用户查询的模糊性和复杂性常导致用户意图与检索到的商品标题或文档之间不匹配。现有方法多采用基于Transformer的模型，这些模型在预训练阶段需要数百万标注的查询-标题对，且此类数据通常未考虑用户意图。为解决此问题，我们从eBay现有数据集中筛选样本，并人工标注以买家为中心的相关性分数和中心性分数，以反映商品标题与用户意图的匹配程度。我们提出一种面向现有模型的用户意图中心性优化方法，用于优化语义商品搜索中的用户意图表达。为此，我们设计了一种基于双重损失的优化方案，以处理困难负样本——即语义相关但未反映用户意图的商品标题。本研究的贡献包括构建具有挑战性的评估数据集、实现用户意图中心性优化方法，并在多种评估指标上观察到商品排序效率的显著提升。我们的工作致力于确保与查询最相关的买家导向型标题获得更高排名，从而提升电子商务平台的用户体验。