Movie Recommendation System

Recommendation systems are at the heart of several eCommerce and content consumption businesses of the likes of Amazon and Netflix. With time, these systems have evolved to deliver highly personalized recommendations to users and have become an effective way to increase user retention on said services.

This project explores ways to implement such recommendation systems by leveraging existing data about the users’ past choices. We try to solve this problem through a user-based collaborative-filtering approach. We then briefly explore some shortcomings of the approach and attempt to come up with improvements to it.

Description of Files

• RS_main.py - The main file to run recommender system including user-item matrix generation, neighborhood generation, prediction and and performance evaluation. This should take command line arguments: input as rating.csv and save the output of MAE performance evaluation in eval.csv.

• test.py - This file will take input as a list of test user (already saved by students in test user.txt) and save output in output.csv

• rating.csv - Contains movie ratings given by users.

• eval.csv - Contains the result of MAE performance evaluation performed when running RS_main.py.

• output.csv - Contains top-5 movie recommendations for every user. There is a single movie-recommendation per row (i.e. there will be five rows per user id)

How to run

You will need to install dependencies before running the project, so maybe make sure you're in a python virtual environment or a conda environment before you proceed. The first thing to do is to navigate to the project directory -

dushyant@ubuntu:~$ cd Recommender-System

Using run.sh

Ideally, executing run.sh in the project directory should run the project and display results. This script will install the dependencies if not already installed and then run the project.

You'll probably need to give it executable permissions first.

dushyant@ubuntu:~/Recommender-System$ sudo chmod +x ./run.sh

To run the project as implemented in Part-A (i.e. without the improvements), just run the script as is.

dushyant@ubuntu:~/Recommender-System$ ./run.sh

To run the project as implemented in Part-B (i.e. with the improvements), run the script with the --improved flag.

dushyant@ubuntu:~/Recommender-System$ ./run.sh --improved

This script is tested for Linux (Ubuntu). If it doesn't run on Windows (or doesn't run on your Linux system for some reason), you can still run the project manually as illustrated below.

Running manually

Running Part-A

To run Part-A of the assignment (i.e. original, bare-bones recommendation system without improvements), run the following commands -

1. Run RS_main.py using ratings.csv as the user-ratings dataset and storing the output in eval.csv.

dushyant@ubuntu:~/Recommender-System$ python3 RS_main.py --input ratings.csv --output eval.csv

2. Run test.py with the file test_user.txt as input, storing the output in the file output.csv.

dushyant@ubuntu:~/Recommender-System$ python3 test.py --input test_user.txt --output output.csv

Running Part-B

To run Part-B of the assignment (i.e. recommendation system with improvements), just add the content_boost flag to the commands you used in Part-A -

1. Running RS_main.py

dushyant@ubuntu:~/Recommender-System$ python3 RS_main.py --input ratings.csv --output eval.csv --content_boost

2. Running test.py

dushyant@ubuntu:~/Recommender-System$ python3 test.py --input test_user.txt --output output.csv --content_boost