论文标题
Listbert：学习使用ListWise Bert排名电子商务产品
ListBERT: Learning to Rank E-commerce products with Listwise BERT
论文作者
论文摘要
有效的搜索是具有无数产品数量的电子商务平台的关键组件。每天数百万用户搜索与其需求有关的产品。因此，在顶部显示相关产品将增强用户体验。在这项工作中，我们提出了一种新颖的方法，即给定用户查询，将基于变压器的模型与各种列表损失功能融合在一起，以排名电子商务产品。我们在时尚电子商务语料库上预先培训了罗伯塔模型，并使用不同的列表损失功能对其进行微调。我们的实验表明，与其他流行的ListWisise损失函数（如ListNet和ListMle）相比，使用基于NDCG的替代损失函数（ACTNDCG）微调的Roberta模型可实现13.9％的NDCG改善。与基于成对的RankNet的Roberta模型相比，基于大约的Roberta模型也可以提高NDCG 20.6％。我们称我们以端到端的方式将直接优化罗伯塔模型的方法称为列表替代损失函数作为listbert。由于在实时搜索设置中存在较低的延迟要求，因此我们通过使用知识蒸馏技术来学习以表示为中心的学生模型来轻松地采用这些模型，该模型可以轻松地部署，并导致排名延迟降低约10倍。
Efficient search is a critical component for an e-commerce platform with an innumerable number of products. Every day millions of users search for products pertaining to their needs. Thus, showing the relevant products on the top will enhance the user experience. In this work, we propose a novel approach of fusing a transformer-based model with various listwise loss functions for ranking e-commerce products, given a user query. We pre-train a RoBERTa model over a fashion e-commerce corpus and fine-tune it using different listwise loss functions. Our experiments indicate that the RoBERTa model fine-tuned with an NDCG based surrogate loss function(approxNDCG) achieves an NDCG improvement of 13.9% compared to other popular listwise loss functions like ListNET and ListMLE. The approxNDCG based RoBERTa model also achieves an NDCG improvement of 20.6% compared to the pairwise RankNet based RoBERTa model. We call our methodology of directly optimizing the RoBERTa model in an end-to-end manner with a listwise surrogate loss function as ListBERT. Since there is a low latency requirement in a real-time search setting, we show how these models can be easily adopted by using a knowledge distillation technique to learn a representation-focused student model that can be easily deployed and leads to ~10 times lower ranking latency.
