Abstract
A product recommendation system is aimed at providing an improved shopping experience to the users, thereby increasing the revenues. The text-based product search on most of the online stores is based on the past search history of the user and/or the other characteristics associated to the products, such as the annotated labels, price range, category, description, size, color, and other attributes. Though, this traditional method has adequate performance, however, it is prone to the issues pertaining to improper annotation of the metadata related to the products. Due to the recent advancements in machine learning, product recommendation using machine learning is getting increasing attention. Over the last one decade, tremendous progress in machine learning and computing has paved the way for efficient product recommendation. In this book chapter, we present a critical analysis of product recommendation using the traditional methods. Specifically, the book chapter explores the prospect of product recommendation based on visual similarity using machine learning which is rather a new concept in this domain and is getting increasing attention in research community. The chapter also explores the underlying challenges and discusses the future directives.
Published Version
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