Reference number : GC-2017-Q1-00408, amendment number : , agreement type : G, recipient legal name : Veneris, Andreas (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : In the past decade, retailers have experienced a massive surge in information ranging from how their products_x000D_
sell and which products their customers prefer, to how their promotional campaigns perform. This dramatic_x000D_
change in data availability was mainly due to advances in the Internet-of-Things and data warehousing,_x000D_
allowing millions of customer transactions to be logged in big data centers. This abundance of information,_x000D_
however, cannot easily translate into benefits for retailer and customer, unless it is rigorously analyzed to guide_x000D_
business decisions. It comes as no surprise that Big Data analytics are now becoming a business imperative in_x000D_
the realm of retail, with more and more retailers applying data mining technologies to transform their_x000D_
processes, from product recommendations to sales forecasting. Still, a large amount of the raw data available to_x000D_
retailers is incomplete and generic to justify meaningful analysis. For example many products are only_x000D_
characterized by type, price, color etc., whereas information that tells if the product is more appealing to_x000D_
specific ages, or that is more of a luxury product or not, is usually absent. It is entirely up to the manual effort_x000D_
of human experts to identify these "hidden" attributes, but this incurs significant costs and delays to the retailer_x000D_
or any third party that offers services on big data analytics. The goal of this work is to develop novel_x000D_
methodologies based on machine learning technologies to automatically discover these hidden attributes with_x000D_
minimal human intervention. These methods will learn non-obvious information by mining text and reasoning_x000D_
around images associated with each product, combining techniques from Natural Language Processing and_x000D_
Computer Vision. Through this process one can produce enriched product ontologies that allow for efficient_x000D_
market-driven analytics., program purpose french : In the past decade, retailers have experienced a massive surge in information ranging from how their products_x000D_
sell and which products their customers prefer, to how their promotional campaigns perform. This dramatic_x000D_
change in data availability was mainly due to advances in the Internet-of-Things and data warehousing,_x000D_
allowing millions of customer transactions to be logged in big data centers. This abundance of information,_x000D_
however, cannot easily translate into benefits for retailer and customer, unless it is rigorously analyzed to guide_x000D_
business decisions. It comes as no surprise that Big Data analytics are now becoming a business imperative in_x000D_
the realm of retail, with more and more retailers applying data mining technologies to transform their_x000D_
processes, from product recommendations to sales forecasting. Still, a large amount of the raw data available to_x000D_
retailers is incomplete and generic to justify meaningful analysis. For example many products are only_x000D_
characterized by type, price, color etc., whereas information that tells if the product is more appealing to_x000D_
specific ages, or that is more of a luxury product or not, is usually absent. It is entirely up to the manual effort_x000D_
of human experts to identify these "hidden" attributes, but this incurs significant costs and delays to the retailer_x000D_
or any third party that offers services on big data analytics. The goal of this work is to develop novel_x000D_
methodologies based on machine learning technologies to automatically discover these hidden attributes with_x000D_
minimal human intervention. These methods will learn non-obvious information by mining text and reasoning_x000D_
around images associated with each product, combining techniques from Natural Language Processing and_x000D_
Computer Vision. Through this process one can produce enriched product ontologies that allow for efficient_x000D_
market-driven analytics., agreement title english : Multimodal representation learning for retail product ontology, agreement title french : Multimodal representation learning for retail product ontology, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-04-25, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada