Multimodal representation learning for retail product ontology

点击翻译按钮获取中文摘要

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.