A Smart System for Selection of Optimal Product Images in E-Commerce
| Autor: | Venkatesh Kandaswamy, Abon Chaudhuri, Aditya Subramanian, Samrat Kokkula, Alessandro Magnani, Paolo Messina, Abhinandan Krishnan, S. Gandhi |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Customer engagement Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer science Computer Vision and Pattern Recognition (cs.CV) media_common.quotation_subject Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) 02 engineering and technology E-commerce 01 natural sciences Machine Learning (cs.LG) 010104 statistics & probability Statistics - Machine Learning Human–computer interaction 0202 electrical engineering electronic engineering information engineering Quality (business) Product (category theory) 0101 mathematics media_common Smart system Contextual image classification business.industry Artificial Intelligence (cs.AI) Scalability 020201 artificial intelligence & image processing business |
| Zdroj: | IEEE BigData |
| Popis: | In e-commerce, content quality of the product catalog plays a key role in delivering a satisfactory experience to the customers. In particular, visual content such as product images influences customers' engagement and purchase decisions. With the rapid growth of e-commerce and the advent of artificial intelligence, traditional content management systems are giving way to automated scalable systems. In this paper, we present a machine learning driven visual content management system for extremely large e-commerce catalogs. For a given product, the system aggregates images from various suppliers, understands and analyzes them to produce a superior image set with optimal image count and quality, and arranges them in an order tailored to the demands of the customers. The system makes use of an array of technologies, ranging from deep learning to traditional computer vision, at different stages of analysis. In this paper, we outline how the system works and discuss the unique challenges related to applying machine learning techniques to real-world data from e-commerce domain. We emphasize how we tune state-of-the-art image classification techniques to develop solutions custom made for a massive, diverse, and constantly evolving product catalog. We also provide the details of how we measure the system's impact on various customer engagement metrics. Accepted in IEEE Big Data Conference 2018 (Industry & Government Track) |
| Databáze: | OpenAIRE |
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