There are two main types of recommendation systems: content-based and collaborative filtering. Content-based systems create a user profile based on explicit ratings or implicit actions and use that profile to recommend items similar to what the user has liked in the past. Collaborative filtering systems classify users into clusters of similar users and recommend items to a given user based on what similar users in that cluster prefer.

1. RECOMANDATION
SYSTEM
Name: Lalit Shravan Raut
Roll No. 41

2. There are two
types of
recommendatio
n system
Content Based
Collaborative filtering

3. Content based
recommendation
system
• A Content-Based Recommender works by the
data that we take from the user, either explicitly
(rating) or implicitly (clicking on a link). By the
data we create a user profile, which is then used
to suggest to the user, as the user provides more
input or take more actions on the
recommendation, the engine becomes more
accurate.

4. Collaborative
filtering
• In Collaborative Filtering, we tend to
find similar users and recommend
what similar users like. In this type of
recommendation system, we don’t
use the features of the item to
recommend it, rather we classify the
users into clusters of similar types
and recommend each user according
to the preference of its cluster.

5. THANKS