MovieRecom

This notebook covers different techniques to build movie recommendation systems while attempting to highlight core differences and find the one with best performance.

##Author Anmol Jain
Che-Yuan Liang
Malvin De Nunez Estevez

##NBViewer link offline reading mode link

##How to run the code

1. Download “data” file from the link. Make sure it is saved as “data” folder.

2. Store the data file in the same folder as the MovieRecom_FinalReport.ipynb

3. You might need to install the following pyFM

    pip install git+https://github.com/coreylynch/pyFM
   

4. Open the MovieRecom_FinalReport.ipynb and run it cell by cell

####References

1. F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
2. Factorization Machines paper
3. Fast Context-aware Recommendations with Factorization Machines
4. From Matrix Factorization to Factorization Machines
5. pyFM