Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning
This work addresses relevance modeling for search engines, specifically in the Alipay mini apps search scenario, by incorporating auxiliary user behavior data to improve accuracy and robustness, representing an incremental advancement over conventional semantic-only approaches.
The paper tackled the problem of relevance modeling in search engines by proposing a Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that integrates user behavior data with semantic matching, achieving promising performance with low latency in experiments on real-world industry data and online A/B testing.
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.