How does it work?
We are using transfer learning from the field of image classification to extract features from new data.
What is transfer learning?
There is no need for a priori data thanks to transfer learning. The only required component from stores is a database of the product images to feed the model, so it's possible to build a working demo of this recommendation system for your store within a day.
Adding filtering based on attributes would enhance the performance of the system. For example if we filter by category, we can recommend products for men and women separately from the category. This way, a men’s t-shirt with flowers will not be recommended as a similar product for a woman viewing t-shirts with flowers for women. Product can be improved in the future given feedback is collected based on user clicks on the website. Items clicked from recommendation would be considered a successfully recommended item and therefore the system could learn from user engagement with the given recommendation.
This system can be used to recommend visually similar products when looking for alternatives to the currently viewed item. It's perfect for a starting point, a so called cold start, in behavioral recommendations when there is not sufficient data gathered yet to suggest items based on user's search history.