Product ranking is a crucial component for many e-commerce services. One of the major challenges in product search is the vocabulary mismatch between query and products, which may be a larger vocabulary gap problem compared to other information retrieval domains. While there is a growing collection of neural learning to match methods aimed specifically at overcoming this issue, they do not leverage the recent advances of large language models for product search. On the other hand, product ranking often deals with multiple types of engagement signals such as clicks, add-to-cart, and purchases, while most of the existing works are focused on optimizing one single metric such as click-through rate, which may suffer from data sparsity. In this work, we propose a novel end-to-end multi-task learning framework for product ranking with BERT to address the above challenges. The proposed model utilizes domain-specific BERT with fine-tuning to bridge the vocabulary gap and employs multi-task learning to optimize multiple objectives simultaneously, which yields a general end-to-end learning framework for product search. We conduct a set of comprehensive experiments on a real-world e-commerce dataset and demonstrate significant improvement of the proposed approach over the state-of-the-art baseline methods.
翻译:产品排名是许多电子商务服务的关键组成部分。产品搜索的主要挑战之一是查询和产品之间的词汇不匹配,这与其他信息检索领域相比,可能是一个更大的词汇差距问题。虽然越来越多的神经学习收集与专门旨在克服这一问题的方法相匹配,但它们并没有利用大语言模型的最新进展来进行产品搜索。另一方面,产品排名往往涉及多种类型的参与信号,如点击、添加到cart和采购,而大多数现有作品侧重于优化单一指标,如可能因数据紧张而受到影响的点击通速率。在这项工作中,我们提出了与德国应急和应急专家中心进行产品排名的新颖的端到端多任务学习框架,以应对上述挑战。拟议的模型利用特定领域的BERT,对缩小词汇差距进行微调,并采用多任务学习,同时优化多个目标,从而形成一个一般端到端学习的产品搜索框架。我们对现实世界电子商务数据集进行了一套全面试验,并展示了拟议的基准方法在状态上的重大改进。