Multimodal Representation Learning for Retail Product Ontology

在过去的十年里，零售商经历了大量的信息浪潮，从他们的产品如何销售，他们的客户喜欢哪些产品，到他们的促销活动如何进行。数据可用性的巨大变化主要是由于物联网和数据仓库的进步，使得数十亿的客户交易记录在大数据中心。然而，这些丰富的信息并不能轻易地转化为零售商和顾客的利益，除非这些信息经过严格的分析，以指导商业决策。随着越来越多的零售商应用数据挖掘技术来改变他们的流程，从产品推荐到销售预测，大数据分析正在成为零售领域的商业必需品，这一点也不奇怪。尽管如此，零售商可获得的大量原始数据**是不完整的，而且过于笼统，无法证明有意义的分析是合理的。例如，许多产品的特征只有名称、类型和简短的文字描述，而诸如典型的购买模式、产品吸引的人口统计数据以及它是否是奢侈品牌等属性通常都没有。识别这些“隐藏”属性完全取决于人类专家的手工工作，但这会给零售商或任何提供大数据分析服务的第三方带来巨大的成本和延迟。这项工作的目标是开发基于机器学习技术的新方法，以最少的人为干预自动发现这些隐藏的属性。这些方法将通过挖掘与每个产品相关的文本和图像来学习不明显的信息，这将涉及自然语言处理和计算机视觉技术的结合。通过这个过程，可以生成丰富的产品本体，允许进行有效的市场驱动分析。

In the past decade, retailers have experienced a massive surge of information ranging from how their products**sell and which products their customers prefer, to how their promotional campaigns perform. This dramatic**change in data availability was mainly due to advances in the Internet-of-Things and data warehousing,**allowing billions of customer transactions to be logged in big data centers. This abundance of information,**however, cannot easily translate into benefits for the retailer and customer unless it is rigorously analyzed to**guide business decisions. It comes as no surprise that Big Data analytics are now becoming a business**imperative in the realm of retail, with more and more retailers applying data mining technologies to transform**their processes, from product recommendations to sales forecasting. Still, a large amount of the raw data**available to retailers is incomplete and too generic to justify meaningful analysis. For example, many products**are characterized only by name, type, and a brief textual description, whereas attributes such as typical**purchase patterns, the demographics to which the product appeals, and whether it is a luxury brand are usually**absent. It is entirely up to the manual effort of human experts to identify these "hidden" attributes, but this**incurs significant costs and delays to the retailer or any third party that offers services on big data analytics. The**goal of this work is to develop novel methodologies based on machine learning technologies to automatically**discover these hidden attributes with minimal human intervention. These methods will learn non-obvious**information by mining text and images associated with each product, which will involve a combination of**techniques from Natural Language Processing and Computer Vision. Through this process one can produce**rich product ontologies that allow for effective market-driven analytics.