Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning

TL;DR

This paper introduces BARL-ASe, a behavior-augmented relevance model that combines semantic matching with user behavior data, improving search relevance in Alipay's mini app ecosystem through multi-level co-attention and self-supervised learning.

Contribution

The paper presents a novel relevance learning model that integrates neighbor query and item information with semantic matching, enhancing search accuracy and robustness in industrial applications.

Findings

Achieves improved relevance performance on real-world data.

Demonstrates effectiveness through online A/B testing.

Handles long-tail query-item matching effectively.

Abstract

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.