· 1 min read

Applications of Recommender Systems



Photo by Charles Deluvio on Unsplash

Recommender systems are easiest and probably most lucritive ML Application.

The whole of social media, content sites and E-Commerce Companies are built around it.

The goal is said to be to provide right information at the right time and avoiding information overload. The statement seems ideal and a recommender system can be made to work such a way.

Most often for consumer facing products, the real reason is the opposite. Keep people hooked to using the product for longer.

In social media, it would be used to recommended content/people similar to once you are viewing or something you might like.

For E-Commerce companies, the focus is on recommended you the product you’ll likely purchase.

Recommender systems are also used at a smaller scale like Keyboard Suggestions on your phone.

For Enterprise, recommender systems can help improve productivity by a lot.

  • By having documents/data grouped, the right information can found faster.
  • By having similar documents groups/recommended, comparing between documents can be made simpler.

Recommender Systems often require much less data to operate than other Machine/Deep Learning problems.

Back to Blog