  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.
