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.

在电子商务搜索引擎中，语义相关性计算对于确保所选项目与客户意图紧密对齐至关重要。本文介绍了一种基于交互的建模范式，通过动态长度表示方案、专业术语识别方法和对抗性训练协议来增强模型的鲁棒性和匹配能力。离线评估表明我们方法的卓越鲁棒性和效果，在线 A/B 测试证实了在相同曝光位置提高相关性、增加点击和转化的能力。据我们所知，这是第一个用于大型电子商务搜索相关性计算的基于交互的方法。

面向在线电子商务和基于 LLM 的检索的鲁棒性基于交互关联建模

Robust Interaction-based Relevance Modeling for Online E-Commerce and  LLM-based Retrieval

Learning to rank is a key component of many e-commerce search engines. In
learning to rank, one is interested in optimising the global ordering of a list
of items according to their utility for users.Popular approaches learn a
scoring function that scores items individually (i.e. without the context of
other items in the list) by optimising a pointwise, pairwise or listwise loss.
The list is then sorted in the descending order of the scores. Possible
interactions between items present in the same list are taken into account in
the training phase at the loss level. However, during inference, items are
scored individually, and possible interactions between them are not considered.
In this paper, we propose a context-aware neural network model that learns item
scores by applying a self-attention mechanism. The relevance of a given item is
thus determined in the context of all other items present in the list, both in
training and in inference. We empirically demonstrate significant performance
gains of self-attention based neural architecture over Multi-LayerPerceptron
baselines, in particular on a dataset coming from search logs of a large scale
e-commerce marketplace, Allegro.pl. This effect is consistent across popular
pointwise, pairwise and listwise losses.Finally, we report new state-of-the-art
results on MSLR-WEB30K, the learning to rank benchmark.

提出了一种基于自注意力机制的上下文感知神经网络模型，用于学习商品的排序分数，在训练和推理阶段都考虑了列表中其他商品的交互作用，相较于多层感知机模型，取得了显著的性能提升，在排序学习基准 MSLR-WEB30K 上实现了新的最佳结果。