论文标题
电子商务非默认搜索排名的微调BERT
Fine-tune BERT for E-commerce Non-Default Search Ranking
论文作者
论文摘要
电子商务平台上的非默认排名质量（例如基于上升商品价格或下降的历史销售量）通常会遇到急性相关性问题，因为无关的项目更容易在排名结果的顶部公开。在这项工作中，我们提出了一个两阶段的排名方案，该方案首先通过精制查询/标题关键字匹配来召回广泛的候选项目，然后使用人类标签数据中的bert-large微调对召回的项目进行分类。我们还对多个GPU主机和TensorFlow的C ++代币化自定义OP实现了并行预测。在这一数据挑战中，我们的模型在监督阶段（基于总体F1分数）和在最后阶段（基于平均每个查询F1分数）赢得了第一名。
The quality of non-default ranking on e-commerce platforms, such as based on ascending item price or descending historical sales volume, often suffers from acute relevance problems, since the irrelevant items are much easier to be exposed at the top of the ranking results. In this work, we propose a two-stage ranking scheme, which first recalls wide range of candidate items through refined query/title keyword matching, and then classifies the recalled items using BERT-Large fine-tuned on human label data. We also implemented parallel prediction on multiple GPU hosts and a C++ tokenization custom op of Tensorflow. In this data challenge, our model won the 1st place in the supervised phase (based on overall F1 score) and 2nd place in the final phase (based on average per query F1 score).
