Building a hybrid recommmendation system

Recommendation systems are widely used in e-commerce companies like Amazon and Netflix to help users discover items that they might not have found by themselves. Due to their wide applicability, recommendation systems have become an area of active research. We have done a comparative study on the different algorithms used to do recommendation popularly and built a hybrid model out of them.

Our main focus is on recommending movies and we have tested our algorithms on MovieLens dataset

We have examined the following algorithms

• Slope One
• K nearest neighbours
• Singular Value Decomposition (SVD)
• Incremental SVD
• Incremental SVD with Temporal Dynamics
• Content based
• Demographic based

For detailed information, please see the Project Report