recommender_system

by Yakup Çatalkaya

Requirements

os
sys
getopt
requests
shutil
numpy
pandas
matplotlib
zipfile
keras
tensorflow

Question 1

For dependencies, you should install all requirements by writing

pip install requirements.txt

I have chosen MovieLens 25M Dataset. The first 1M line contains 6748 user and 21952 movies and corresponding ratings. All Nan values filled with 0.0 in the sparse matrix. "ratings.csv" contains userId, movieId, rating, timestamp columns. The lines within this file are ordered first by userId, then, within user, by movieId. Ratings are made on a 5-star scale, with half-star increments (0.5 stars - 5.0 stars).

I have chosen matrix factorization method to capture interactions between users and items.

For loss function, I have used Mean Squared Error (MSE) and tried to optimize it.

By using Tensorflow, I have created Input, Embedding and Flatten layers for recommendation system. After that, I have created Simple Feed-Forward Neural Network stacked to Embedding layer. By splitting data into train and validation set, I havel also tried to tune hyperparameter.

To train a model, -t True should be written.

To give a new path, -p 'path/to/file' should be written.

To specify userId, -u anyInteger should be written.

To extract dataset from zip file, -e True should be written.

To download the dataset from its source, -d True should be written.

To get top 5 recommended movies, you should just write:

python recommend.py -u 123

To get top 5 recommended movies, you should write (with all parameters):

python recommend.py -t True -p 'path/to/file' -u 123 -e True -d True

Question 2

The deployable deficiency of the model is the dataset does not contain important formats such as age values. The age value is important to filter out the adult contents for nonadult users. Therefore, the recommendation system could cause legal problems. There could be another csv file that gives us the information about the adult/general content label. So we can ignore adoult content while recommending a movie to a nonadult user.

Reference

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872