Product classification using AI and Machine Learning
Posted on Jan 30th 2021
There are numerous situations where precisely recognizing an item is essential. Stores might analyze contender costs for precisely the same products. Customers use price comparison instruments to get the best deals according to their budget. A store like Amazon that permits various merchants to offer the same products needs to be sure that they are the same products before posting and providing a unique product page.
Products in different stores do not have a standardized title or description. Each online store and other sellers within a store might have varying product titles for the same product. There are specific attributes for product posting according to the website's criteria like image dimensions, description length, etc. But these images might differ in size, clarity, or tone. Also, the brand name can be different for similar products, like GE for General Electric.
To solve such issues, knowledge of computer technology is employed.
Machine Learning for product match
For product matching solutions in machine learning, a database of billions of products needs to be collected first. This data is collected by web crawlers and feeds.
A universal taxonomy is needed for this kind of system. It is a challenging job because different retailers use varying classification methods, and one product might be listed in more than one category. For example, a specific shoe model may be listed under games shoes and men's jogging shoes at the same time. The Product Match framework initially needs to plan an advanced categorization system, regardless of how a specific store characterizes its items.
There are standard listing models like Google Taxonomy, GS1, and Amazon. However, an item coordinate arrangement may devise its scientific categorization. This categorization is planned by distinguishing products from signs and titles, item descriptions, attributes, and pictures.
Once characterization is finalized, then comes matches of products. At this stage, there is a need for precise comparisons of different products to be categorized in specific lists. Despite the differences in images, titles, and descriptions, they can be classified into the same groups if they are similar in specifications and quality.
Deep learning and neural networks allow us to identify them based on their similarities. For learning and identification, word-level embedding is created. In some cases, the product title provides a lot of information to be consumed by the system. Then it decides the type and classifies it according to the product name and other attributes.
All this information is collected by the system and sorted out accordingly, and then it is put into appropriate slots for future references. In this way, the machine plays a crucial role in product classification and helps make the user experience better online.