Result relevance scoring is critical to e-commerce search user experience. Traditional information retrieval methods focus on keyword matching and hand-crafted or counting-based numeric features, with limited understanding of item semantic relevance. We describe a highly-scalable feed-forward neural model to provide relevance score for (query, item) pairs, using only user query and item title as features, and both user click feedback as well as limited human ratings as labels. Several general enhancements were applied to further optimize eval/test metrics, including Siamese pairwise architecture, random batch negative co-training, and point-wise fine-tuning. We found significant improvement over GBDT baseline as well as several off-the-shelf deep-learning baselines on an independently constructed ratings dataset. The GBDT model relies on 10 times more features. We also present metrics for select subset combinations of techniques mentioned above.

中文翻译：

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统一的神经网络方法进行电子商务相关学习

结果相关性评分对于电子商务搜索用户体验至关重要。传统的信息检索方法侧重于关键字匹配以及基于手工或基于计数的数字特征，对项目语义相关性的了解有限。我们描述了一种高度可扩展的前馈神经模型，以仅使用用户查询和项目标题作为特征，并同时使用用户点击反馈和有限的人类评分作为标签来提供（查询，项目）对的相关性得分。应用了一些常规增强功能来进一步优化评估/测试指标，包括暹罗成对的体系结构，随机批次负协同训练和逐点微调。我们发现，在独立构建的评级数据集上，GBDT基准以及一些现成的深度学习基准均得到了显着改善。GBDT模型依赖于10倍以上的功能。我们还介绍了上述技术的选定子集组合的指标。