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To achieve truly personalized experience, one has to know individual user's choices or preferences in detail. Learn how you can do that with this recommendation…

The e-commerce boom makes the online environment more competitive. Internet retailers seek competitive advantages and a personalized experience for their clients. To achieve a truly personalized experience, you must know an individual user’s choices or preferences in detail. How cool would it be if you have a recommendation system that recommends products based on a person’s features (like their age or gender)?

A good recommendation system can typically boost your sales. When it comes to apparel or jewelry, using a good recommendation engine with images is very important. Usually, people show greater interest in the products when they get a personalized recommendation.

For example, if a person wants to buy a piece of jewelry or eye glasses, they would select an item that they like or that looks good on them. So, providing a personalized recommendation of products based on their features helps them in getting a product that they would like.

To help with this, we can use the Watson™ Visual Recognition service. This service helps you with recognizing a person’s features such as their age or gender by using the image of the person as input. We developed a hybrid mobile application that uses MobileFirst Foundation integrated with a recommendation system that takes an image of the user as input and detects their features with the help of a Watson Visual Recognition model. Based on these features, the recommendation engine returns a personalized recommendation of jewelry products. This can be extended to any other product.

This recommendation engine is implemented using an IBM Cloud account, Python 3, and Java 1.8.0.

We have created a sample application for you that you can use as a foundation and build on top of it. Check out Recommendation system based on visual recognition. It takes approximately 15 minutes to implement it.

This blog is just an introduction to how we can use Watson Visual Recognition for personalized recommendations. To explore more examples, check out Integrate a virtual mirror with e-commerce products and Build a ‘try-and-buy’ mobile application with augmented reality capabilities for a furniture store.

Have fun!

Rahul Reddy Ravipally