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).
翻译:在电子商务平台上非违约排名的质量,例如根据上扬的项目价格或历史销售量下降,往往受到尖锐的相关问题,因为这些无关的项目更容易在排名最高的结果上曝光。在这项工作中,我们提出一个分为两阶段的排名办法,首先通过改进的查询/标题关键词匹配来回顾范围广泛的候选项目,然后利用对人类标签数据进行微调的BERT-Large来分类被召回的项目。我们还对多个GPU主机进行了平行预测,并对Tensorprox的C++代谢性定制操作进行了平行预测。在这一数据挑战中,我们的模型在监督阶段赢得了第1位(以总的F1评分为基础),在最后阶段赢得了第2位(以每个查询的F1分平均数为基础)。