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
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Download “data” file from the link. Make sure it is saved as “data” folder.
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Store the data file in the same folder as the MovieRecom_FinalReport.ipynb
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You might need to install the following pyFM
pip install git+https://github.com/coreylynch/pyFM
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Open the MovieRecom_FinalReport.ipynb and run it cell by cell
####References
- 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
- Factorization Machines paper
- Fast Context-aware Recommendations with Factorization Machines
- From Matrix Factorization to Factorization Machines
- pyFM