论文标题
学习可用于电子商务产品搜索的强大模型
Learning Robust Models for e-Commerce Product Search
论文作者
论文摘要
显示不匹配搜索查询意图的项目会降低电子商务中的客户体验。这些不匹配是由对噪声行为信号（例如点击和搜索日志中的购买）的反事实偏差引起的。缓解问题需要一个大型标记的数据集，这是昂贵且耗时的。在本文中，我们开发了一个深刻的端到端模型，该模型学会了有效地对不匹配进行分类并产生困难的示例以改善分类器。我们通过将潜在变量引入跨凝结损失，该损失在使用真实样品和生成的样品之间交替训练模型。这不仅使分类器更加强大，而且还可以提高整体排名表现。与基准相比，我们的模型在F评分中获得了相对增长，而PR曲线下的面积超过17％。在实时搜索流量中，我们的模型在多个国家 /地区获得了重大改进。
Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain. In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier. We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples. This not only makes the classifier more robust but also boosts the overall ranking performance. Our model achieves a relative gain compared to baselines by over 26% in F-score, and over 17% in Area Under PR curve. On live search traffic, our model gains significant improvement in multiple countries.
