Relevance modeling aims to locate desirable items for corresponding queries, which is crucial for search engines to ensure user experience. Although most conventional approaches address this problem by assessing the semantic similarity between the query and item, pure semantic matching is not everything. In reality, auxiliary query-item interactions extracted from user historical behavior data of the search log could provide hints to reveal users' search intents further. Drawing inspiration from this, we devise a novel Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that leverages neighbor queries of target item and neighbor items of target query to complement target query-item semantic matching. Specifically, our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views. The model subsequently employs neighbor-target self-supervised learning to improve the accuracy and robustness of BARL-ASe by strengthening representation and logit learning. Furthermore, we discuss how to deal with the long-tail query-item matching of the mini apps search scenario of Alipay practically. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance with low latency.

翻译：相关性建模旨在为相应查询定位所需物品，这对搜索引擎保障用户体验至关重要。尽管大多数传统方法通过评估查询与物品的语义相似性来解决该问题，但纯语义匹配并非万能。实际上，从搜索日志中用户历史行为数据提取的辅助查询-物品交互，能进一步揭示用户搜索意图的线索。受此启发，我们为支付宝搜索设计了一种新型行为增强相关性学习模型（BARL-ASe），该模型利用目标物品的邻居查询和目标查询的邻居物品来补充目标查询-物品的语义匹配。具体而言，我们的模型构建了多层级协同注意力机制，用于从邻居视图和目标视图中提取粗粒度和细粒度语义表示。随后，模型采用邻居-目标自监督学习，通过强化表示与逻辑值学习来提升BARL-ASe的准确性与鲁棒性。此外，我们还探讨了如何实际处理支付宝小程序搜索场景中的长尾查询-物品匹配问题。在真实行业数据和线上A/B测试上的实验表明，我们的方案在低延迟条件下取得了令人满意的性能。