Movie Recommendation Engine

Collaborative Filtering

• Collaborative Filtering simply put uses the "wisdom of the crowd" to recommend items.
• Item based collaborative filtering uses the patterns of users who liked the same movie as me to recommend me a movie (users who liked the movie that I like, also liked these other movies).
• Recommendation based on user's input of any movie present in the dataset.

Files

• Movie_Recommender_Notebook.ipynb: Jupyter notebook with step-by-step instructions
• Movie_Recommender_User_Input.py: Python file for allowing user's input of movie

Dataset

The following main data source was used for this project:

• MovieLens 100K

How does it work?

Data Pre-processing

• Dropping columns that are not required
• Merging dataframes

Pivot Table

• Pivot tables give you the ability to look at data in so many different ways.
• Pivot table is created as shown in the image with Movies as rows, Users as columns and Ratings as values.

Cosine Similarity

• Also known as vector-based similarity, this formulation views two items and their ratings as vectors, and defines the similarity between them as the angle between these vectors:

Recommender

• User enters his favourite movie (or the movie on the basis of which he wants the system to recommend movies)
• Based on the user's input, recommendation is made by sorting the similarities in descending order

References

• Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The Adaptive Web (pp. 291-324). Springer Berlin Heidelberg.