Analysis of diffrent recommendation engines for movie recommendation

Personalization: Theory & Applications Project 1

IEOR 4571

Fall 2019

---

Authors:

1. Chandrasekaran Anirudh Bhardwaj (cb3441)

2. Ridhi Mahajan (rm3601)

3. Sheetal Reddy (kr2793)

---

In this project we perform an in-depth analysis of different algorithms for movie recomendation.

The report is in Final.ipynb

Code is structured as follows

.
├── utils
|     ├── data_loader.py			# Load data & Sampling functions
│     └── yapf_format.py			# pep8 code standard
|
├── model
|     ├── baseline_model.py			# Bias based model
|     ├── als_model.py				# Alternating Least Squares based Matrix Factorization
|     ├── lightfm_model.py			# LightFm
│     └── nearest_neighbor_model.py		# Nearest Neighbors model with Z-score scaling of users
├── data                     			# Data files
├── cache                    			# Data cache used to avoid re-reading the data each time model changes are made
├── Final.ipynb                   		# Report Markdown
├── LICENSE
└── README.md