How does Recommendation System work?

There are a lot of applications where websites collect data from their users and use that data to predict the likes and dislikes of their users. This allows them to recommend the content that they like. Recommender systems are a way of suggesting or similar items and ideas to a user’s specific way of thinking.

Recommender System is of different types:

  • Collaborative Filtering: Collaborative Filtering recommends items based on similarity measures between users and/or items. The basic assumption behind the algorithm is that users with similar interests have common preferences.
  • Content-Based Recommendation: It is supervised machine learning used to induce a classifier to discriminate between interesting and uninteresting items for the user.

Why the Recommendation system?

  • Benefits users in finding items of their interest.
  • Help item providers in delivering their items to the right user.
  • Identity products that are most relevant to users.
  • Personalized content.
  • Help websites improve user engagement.