Multimodal representation learning for retail product ontology

在过去的十年里，零售商经历了信息的大规模激增，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。数据可用性的这种戏剧性变化主要是由于物联网和数据仓库的进步，允许数百万客户交易记录在大数据中心。然而，除非经过严格的分析以指导商业决策，否则这些丰富的信息很难转化为零售商和客户的利益。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，大数据分析现在正成为零售领域的一项业务需求，这并不令人意外。尽管如此，零售商可以获得的大量原始数据是不完整和通用的，不足以证明有意义的分析是合理的。例如，许多产品只是通过类型、价格、颜色等来表征，而关于产品是对特定年龄段的人更有吸引力，还是更像是奢侈品的信息通常是缺失的。识别这些“隐藏”属性完全取决于人类专家的人工努力，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误。这项工作的目标是开发基于机器学习技术的新方法，以在最少的人工干预下自动发现这些隐藏的属性。这些方法将通过挖掘文本和围绕与每种产品相关的图像进行推理，结合自然语言处理和计算机视觉的技术来学习不明显的信息。通过这个过程，人们可以生成丰富的产品本体，从而实现高效的市场驱动型分析。

In the past decade, retailers have experienced a massive surge in information ranging from how their productssell and which products their customers prefer, to how their promotional campaigns perform. This dramaticchange in data availability was mainly due to advances in the Internet-of-Things and data warehousing,allowing millions of customer transactions to be logged in big data centers. This abundance of information,however, cannot easily translate into benefits for retailer and customer, unless it is rigorously analyzed to guidebusiness decisions. It comes as no surprise that Big Data analytics are now becoming a business imperative inthe realm of retail, with more and more retailers applying data mining technologies to transform theirprocesses, from product recommendations to sales forecasting. Still, a large amount of the raw data available toretailers is incomplete and generic to justify meaningful analysis. For example many products are onlycharacterized by type, price, color etc., whereas information that tells if the product is more appealing tospecific ages, or that is more of a luxury product or not, is usually absent. It is entirely up to the manual effortof human experts to identify these "hidden" attributes, but this incurs significant costs and delays to the retaileror any third party that offers services on big data analytics. The goal of this work is to develop novelmethodologies based on machine learning technologies to automatically discover these hidden attributes withminimal human intervention. These methods will learn non-obvious information by mining text and reasoningaround images associated with each product, combining techniques from Natural Language Processing andComputer Vision. Through this process one can produce enriched product ontologies that allow for efficientmarket-driven analytics.