Recommendation system using deep learning

Recommendation systems play a significant rolefor users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system.

In the digital world, recommendation systems play a significant role—both for the users and for the company. For users, it opens up a new world of options that were previously tough to find. For companies, it helps drive user engagement and satisfaction, directly impacting the bottom line. If you’ve shopped on an ecommerce site or watched a movie on an on-demand video platform, you’ve seen options like, “People who viewed this product also viewed…” or, “Products similar to this one.…” These are the results from recommendation systems (recsys). Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduced you to deep learning-based approaches. You’ll leave with the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system. Introduction Content based Collaborative filtering Learning to rank Hybrid recommender Time and context Deployment and monitoring Evaluation, challenges, and the way forward