论文标题
证明 - 根据神经语言模型进行自动化产品更换和冷启动的自我监管管道
ProVe -- Self-supervised pipeline for automated product replacement and cold-starting based on neural language models
论文作者
论文摘要
在零售垂直行业中，企业正在处理人类快速理解和适应新购买行为的局限性。此外，零售业务需要克服适当管理大量产品/品牌/类别的人类局限性。这些限制导致商业商业的缺陷（例如销售额损失，客户满意度的降低）和运营的观点（例如，库存，过度储备）。在本文中，我们提出了一种基于自然语言理解的管道方法，以建议对库存外的产品进行最合适的替代品。此外，我们将提出一种用于管理在零售商投资组合中新引入的产品的解决方案，几乎没有交易历史记录。该解决方案将帮助企业：自动将新产品分配给正确的类别；从第1天开始推荐交叉销售的补充产品；即使没有交易历史记录，也要执行销售预测。最后，在基于深度学习的需求预测解决方案中，应用了本文中介绍的管道而产生的矢量空间模型直接用作语义信息，从而导致了更准确的预测。整个研究和实验过程已经使用现实生活中的私人交易数据完成，但是源代码可在https://github.com/lummetry/prove上获得。
In retail vertical industries, businesses are dealing with human limitation of quickly understanding and adapting to new purchasing behaviors. Moreover, retail businesses need to overcome the human limitation of properly managing a massive selection of products/brands/categories. These limitations lead to deficiencies from both commercial (e.g. loss of sales, decrease in customer satisfaction) and operational perspective (e.g. out-of-stock, over-stock). In this paper, we propose a pipeline approach based on Natural Language Understanding, for recommending the most suitable replacements for products that are out-of-stock. Moreover, we will propose a solution for managing products that were newly introduced in a retailer's portfolio with almost no transactional history. This solution will help businesses: automatically assign the new products to the right category; recommend complementary products for cross-sell from day 1; perform sales predictions even with almost no transactional history. Finally, the vector space model resulted by applying the pipeline presented in this paper is directly used as semantic information in deep learning-based demand forecasting solutions, leading to more accurate predictions. The whole research and experimentation process have been done using real-life private transactional data, however the source code is available on https://github.com/Lummetry/ProVe
