  Semantic relevance calculation is crucial for e-commerce search engines, as
it ensures that the items selected closely align with customer intent.
Inadequate attention to this aspect can detrimentally affect user experience
and engagement. Traditional text-matching techniques are prevalent but often
fail to capture the nuances of search intent accurately, so neural networks now
have become a preferred solution to processing such complex text matching.
Existing methods predominantly employ representation-based architectures, which
strike a balance between high traffic capacity and low latency. However, they
exhibit significant shortcomings in generalization and robustness when compared
to interaction-based architectures. In this work, we introduce a robust
interaction-based modeling paradigm to address these shortcomings. It
encompasses 1) a dynamic length representation scheme for expedited inference,
2) a professional terms recognition method to identify subjects and core
attributes from complex sentence structures, and 3) a contrastive adversarial
training protocol to bolster the model's robustness and matching capabilities.
Extensive offline evaluations demonstrate the superior robustness and
effectiveness of our approach, and online A/B testing confirms its ability to
improve relevance in the same exposure position, resulting in more clicks and
conversions. To the best of our knowledge, this method is the first
interaction-based approach for large e-commerce search relevance calculation.
Notably, we have deployed it for the entire search traffic on alibaba.com, the
largest B2B e-commerce platform in the world.
