Multimodal Representation Learning for Retail Product Ontology

零售产品本体的多模态表示学习

• 批准号: 522736-2018
• 负责人: Veneris, Andreas
• 金额: $ 0.91万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=667702
• 关键词: Multimodal; Representation; Learning; Retail; Product

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.

在过去的十年里，零售商经历了大量的信息激增，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，**允许在大数据中心记录数十亿客户交易。然而，这些丰富的信息**除非经过严格的分析以**指导商业决策，否则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**，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**并不完整，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**只通过名称、类型和简短的文字描述来表征，而诸如典型的**购买模式、产品吸引的人口统计学以及它是否是奢侈品牌等属性通常**缺失。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，以便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的**技术的组合。通过这个过程，人们可以产生**丰富的产品本体，从而实现有效的市场驱动型分析。