Nowadays, product recommendation engines can understand customers in real time and suggest the best matching using several different machine learning algorithms.
Unfortunately, many e-commerce stores put basic category levels or best-selling product recommendations on their websites, and never revise them or customize them again. They stand to gain by implementing recommendation engines.
Deep learning works similarly to machine learning models, but deep learning uses more than one million data points and can determine whether the prediction is correct. Since deep learning is a self-learning system, manual intervention is no longer required, so the results can be used immediately.
The core idea of deep learning-based recommendations is to unite all side-information features into subnetworks of user-related and item-related features and learn the similarity between their latent representations using neural matrix factorization.
It has been demonstrated that deep learning with heterogeneous input significantly increases overall recommendation quality which makes it suitable for recommendations with rich side information about items and users.
This SlideShare explores the concept of using deep recommendations for e-commerce in brief.