提升电商搜索排名，让最符合用户意图的商品更靠前。
Centrality-aware Product Retrieval and Ranking
- 引入用户意图中心性优化，用双损失函数处理语义相关但意图不符的干扰项。
- 在eBay数据集上，多指标评估显示排名效率显著提升。
- 适合关注电商搜索体验优化的研究者与工程师。
本文针对电商平台提升用户搜索体验的需求，改进商品标题与用户查询之间的匹配度。用户查询常存在歧义和复杂性，导致检索结果与真实意图不一致。现有基于Transformer的方法依赖数百万标注的查询-标题对进行预训练，但未充分考虑用户意图。为此，我们从eBay数据集中选取样本，人工标注了以买家为中心的相关性得分和中心性得分，反映标题与用户意图的契合程度。提出用户意图中心性优化（UCO）方法，通过双损失机制优化模型对难负样本（语义相关但意图不符）的判别能力。贡献包括构建具有挑战性的评测集并实现UCO，在不同评估指标下均观察到显著的排名效率提升。本工作旨在确保最符合买家意图的商品在搜索结果中排位更高，从而改善电商平台用户体验。
原文摘要 · Abstract (English)
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.
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