Collaborative and content filtering are traditionally used for the Recommender system but multiple domains like NLP, image recognition have seen great success with the deep learning models over traditional models. Amazon, YouTube are powered with complex deep learning systems to deliver very efficient recommender systems. In the fight against COVID-19, economic activities that require close physical contact have been severely restricted. In this context, e-commerce – defined broadly as the sale of goods or services online – is emerging as a major pillar. Most enterprises have started moving to e-commerce platforms to launch themselves in the digital marketplace. Successful e-commerce companies need to leverage personalization technology to give consumers a customized experience. Delivering personal experiences on e-commerce sites is achieved by dynamically showing content, product recommendations, and specific offers based on browsing behavior, previous actions, prior purchase history, customer demographics, and other enriched personal data. Hence the efficient, readily usable recommender system would be the need of the hour. I will explain the use case of how a recommender system can be built when an enterprise has a big product portfolio without explicit customer ratings for products. Additionally, I will talk about, how to build time-based modeling and testing a dataset for training a recommender system without introducing biases and data leakages, what will be the ideal way of measuring the recommendation system model’s accuracy and how entity embeddings improve the recommendation power significantly. I will showcase the comparative analysis of recommender systems over the period leading to improvements in recommendation with different AI algorithms.