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

中文翻译：

---

ProVe——基于神经语言模型的自动产品更换和冷启动的自监督流水线

在零售垂直行业中，企业正在应对快速理解和适应新购买行为的人为限制。此外，零售企业需要克服正确管理大量产品/品牌/类别的人为限制。这些限制导致商业（例如销售损失、客户满意度下降）和运营角度（例如缺货、库存过多）的缺陷。在本文中，我们提出了一种基于自然语言理解的管道方法，用于为缺货的产品推荐最合适的替代品。此外，我们将提出一种解决方案，用于管理几乎没有交易历史的零售商产品组合中新推出的产品。该解决方案将帮助企业：自动将新产品分配到正确的类别；从第 1 天起推荐用于交叉销售的互补产品；即使几乎没有交易历史，也能执行销售预测。最后，通过应用本文提出的管道所产生的向量空间模型直接用作基于深度学习的需求预测解决方案中的语义信息，从而实现更准确的预测。整个研究和实验过程是使用真实的私人交易数据完成的，但是源代码可在 https://github.com/Lummetry/ProVe 上找到 应用本文提出的管道产生的向量空间模型在基于深度学习的需求预测解决方案中直接用作语义信息，从而导致更准确的预测。整个研究和实验过程是使用真实的私人交易数据完成的，但是源代码可在 https://github.com/Lummetry/ProVe 上找到 应用本文提出的管道产生的向量空间模型在基于深度学习的需求预测解决方案中直接用作语义信息，从而导致更准确的预测。整个研究和实验过程是使用真实的私人交易数据完成的，但是源代码可在 https://github.com/Lummetry/ProVe 上找到