To categorize the products and to correctly describe the high-quality descriptions for the items is a real problem in the field of e-commerce. It means that if a person is searching for a specific product on our website, it is difficult for us to show such products to the customers which are according to their desires and needs. In order to find out the possible solution to this problem, we should use corresponding descriptive information in each product. Therefore, we can say that the importance of the rich categorization of the products is essential for us. For this reason, we have to perform manual labour process and we require several people who can describe these products. No doubt, it is a difficult task for us. The only solution to this problem is to categorize this system automatically. Here, we will discuss how machines are helpful for us in describing our products.
As we know that there are lots of developments are done in the field of image categorization. Therefore, it is easy for us to extract the necessary information about our products from the image categorization. Anyhow, in the last two to three years, we are able to get enough success in this field. It means that it is helpful for us to get enough information from DL and CNN. If we talk about CNN, we come to know that CNN builds a hierarchy of layers and each layer of the CNN has the ability to abstract information from the previous layer.
If we have built a layer-wise approach to get an idea about the possible features of a product, it will be easy for us to get an idea about its in-depth features. For example, if we want to get an idea about the possible features of clothing, image categorization will provide us with enough idea about possible features of clothing. For this reason, we just need to use pre-trained models of CNN and these pre-trained models of CNN will be helpful for us in adapting our fashion domains. In order to provide the base to this model, we can use the Deep Learning model that is being used in machines learning process.
A multi-model approach is also helpful for us in describing our products. For example, we have maintained an e-commerce website and on this e-commerce website, there are lots of categories of the products. With the help of this multi-model approach, it is possible for us to categorize the products in their relevant categories. For example, if you have posted an image of the dress on your e-commerce website, this website will automatically arrange this product in the dress’s categories. In this category, if you have created some essential sub-categories, this kind of multi-modal approach will also be helpful to you in detecting the sub-categories of these products and arrange these products in their relevant sub-categories.
Along with these benefits of the multi-model approach, there are also some drawbacks of this approach. First of all, if we have created lots of categories for different kinds of products, their memory costs will be increased. Secondly, there is a possibility that some errors have occurred while detecting the possible categories of the products. Under such a situation, your products will be categorized into different categories. In order to solve these problems, we should take an overview of our multi-model approach on a regular basis.
Due to the possible problems in the multi-model approach, we should pursue a single model approach and this single model approach is known as a hierarchical approach. This kind of machines learning approach is helpful for us in describing the products. For this reason, we are using three output model approach. With the help of this three-model approach, it is possible for us to sub-categorize and attribute of the images of the products. While sharing an image of any product, it will detect the subcategory and attributes of the image.
For this reason, it will create lots of paths and these paths will be helpful for us in creating new links for new paths to describe our products by machines. It means that all the subcategories will be connected with each other and with the help of these subcategories, it will be helpful for us to get help from each other. There are different ways to show predictions in this approach. The correct predictions are shown in the green colour. The incorrect predictions are shown in red colour. On the other hand, the correct predictions that are not mutually annotated in the blue colour. In this way, it is possible for us to increase the visualization of the products.