Recommender

 Recommender system 

 A recommender system is subclass of information filtering that suggest items to a user, tailored to their preferences and also mostly these types of system are useful when the user are given with many items to choose. This recommender system is widely used in online platforms where it suggests many things like music, movies and many other contents to the user according to their preferences.

Two types of recommender system:

1. Collaborative Filtering:

       . User-Based Collaborative Filtering: This is a type of recommender system which finds the similar preferences of the user and suggests them according to it. Suppose, person A likes 1,2,3 movies and person B likes 2,3,4 movies then it suggest person B 4 movies.

      . Item-Based Collaborative Filtering: This is a type of recommender system which find the similarities between the items that the user had liked before and suggest items according to it.

2. Content - Based Filtering:  Content based filtering recommender system is a type of system which focuses on the attributes/features of the items that the user has previously liked or disliked and suggest items similar to it.

Why do we use recommender system.

Recommender System are used for various reasons some of them are:

1. Discovery: The recommender system helps user to discover new and different item which they have not known about it. 

2. Optimized User Journey: This recommender system guides the user in their journey suggesting them with the best item according to their current preferences.

3. Increased Engagement: Here, recommender system can increase user engagement and interaction with application by surfacing relevant  products to the user.

Overall, recommender plays important in this online platforms for enhancing user satisfaction, experiences .