Content-Based-Movie-Recommender-System

This model relies on the similarity of the items being recommended.

(I have used Pandas and Numpy. However others may also use different libraries)

A content based recommender works with data that the user provides, either explicitly movie ratings for the MovieLens dataset. Based on that data, a user profile vector is generated, which is then used to make suggestions to the user.

As the user provides more inputs or takes actions on the recommendations, the weighted average vector of user becomes more accurate thus giving more accurate recommendations when data feeded is more.

Advantages :

Learns user's preferences

Highly personalized for the user

Disadvantages :

Low rated movie recommendation might occur if user choices were poor

Only genres watched and rated by user will be recommended and new genres will be hidden

User profile vector is highly static as the movie watched by user is mostly based on user's mentality and not user's previous history