论文标题
伯特去购物：比较产品表示的分销模型
BERT Goes Shopping: Comparing Distributional Models for Product Representations
论文作者
论文摘要
单词嵌入（例如Word2Vec）已通过〜\ textit {prod2vec}成功地应用于电子商务产品。受到上下文化嵌入带来的几个NLP任务的绩效改进的启发，我们建议将类似Bert的体系结构转移到电子商务：我们的模型-〜 \ TextIt {Prod2bert} - 经过培训，可以通过掩盖的会话建模来生成产品表示。通过对多家商店，不同任务和一系列设计选择的广泛实验，我们可以系统地比较〜\ textit {prod2bert}和〜\ textit {prod2vec}嵌入的准确性：而〜\ textIt {prod2bert}在几种情况下发现了〜\ textIt {prod2bert}，我们在几种情况下都可以很好地表现出重要的表现。最后，我们为从业者提供指南，以在各种计算和数据约束下培训嵌入。
Word embeddings (e.g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}. Inspired by the recent performance improvements on several NLP tasks brought by contextualized embeddings, we propose to transfer BERT-like architectures to eCommerce: our model -- ~\textit{Prod2BERT} -- is trained to generate representations of products through masked session modeling. Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of~\textit{Prod2BERT} and~\textit{prod2vec} embeddings: while~\textit{Prod2BERT} is found to be superior in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models. Finally, we provide guidelines to practitioners for training embeddings under a variety of computational and data constraints.
