Building online
recommendation engines to improve
customer experiences
E-commerce product inventories and availability have grown rapidly. However, if customers can't find what they're looking for on your website, they'll leave the store and buy elsewhere. Better search capabilities on your site are one option, but unfortunately, customers don't have the time to dig through search results or create elaborate filters. But there's another option.
The Challenge
Related product recommendations provide the customer with options for related products that are relevant to his interests and purchase intentions. He could be interested in an item that is out of stock in the size he wants, but for which there is a similar item in the right size. Product recommendations can make that connection for the customer and combat lost sales opportunities for your business. The right product recommendations can also increase cart size and average order value by connecting complementary products.
However, many companies don't have the time to sift through their entire inventory to make these recommendations.
The opportunity for deep learning
Deep learning models empower machines with the ability to understand the content of images and text. These models allow us to automate repetitive tasks, such as matching an entire product inventory, at incredible speed and scale. But understanding what an image is, such as a blue shirt, is only half the battle when it comes to making appropriate recommendations to customers. How can we train a model to find similar products based on that understanding?
Suggested solution
Customer product recommendations can be based on a wide range of important information. Having an understanding of who and what your customers like will always be the foundation for building successful AI-powered product recommendations. Should recommendations be based on similar product descriptions, product images, similar brands/qualities, or perhaps similar customer profiles?
In this case, we are going to focus on the image and text descriptions where Deep Learning has its strengths. Using text and image embeddings, a Deep Learning model can identify the similarities between product images or product descriptions. It does this by learning to create representations of these texts or images and then mapping them to similar representations that the model has learned from.
