Deep Learning Based Movie Recommendation System

Get movie recommendations for specific user from recommendation model that trained on MovieLens-1M dataset.

Requirements

First clone this repo and change directory into this folder. It is highly recommended to use the conda virtual environment to set up an environment with required libraries and dependencies.

• For systems without GPU:

conda env create -f requirements_cpu.yaml

• For systems with GPU:

conda env create -f requirements_gpu.yaml

• COLAB

• Docker

A docker image was created for this project. One can use this image to create container. Image contains ubuntu os with cuda.

docker container run -it furkandurmus/movie_recommendation_system:latest
source activate recom_system
cd src
python main.py --args

How to Use?

To train with default parameters run;

python main.py

Additionaly you can play around with below arguments.

    arg.add_argument('--data_path', type=str, default=Path.home(), 
                    help='Path to the data directory')
    arg.add_argument('--data_url', type=str, default='http://files.grouplens.org/datasets/movielens/ml-1m.zip')
    arg.add_argument('--user_id', type=int, default=1, 
                    help='User ID to recommend movies for')
    arg.add_argument('--model_layers', type=list, default=[128 ,64, 32], 
                    help='Number of layers in each hidden layer with any desired depth')
    arg.add_argument('--epoch_number', type=int, default=50, 
                    help='Number of epochs to train')
    arg.add_argument('--batch_size', type=int, default=4096)
    arg.add_argument('--learning_rate', type=float, default=0.001)
    arg.add_argument('--weight_decay', type=float, default=0.001)
    arg.add_argument('--dropout_rate', type=float, default=0.5)
    arg.add_argument('--opt', type=str, default='adam', choices=['adam', 'sgd', 'rmsprop'])
    arg.add_argument('--scheduler', type=str, default='cosine', choices=['cosine', 'reduce'])
    arg.add_argument('--wandb', type=bool, default=False, 
                    help='Whether to use wandb tool for hyperparameter tuning')
    arg.add_argument('--hp_run_number', type=int, default=200,
                    help='Number of hyperparameter tuning runs')

If you set "wandb" argument as True, Wandb tool will start to try hyperparameters listed in hp_tuner.py below. Otherwise one can directly train the model via arguments above.

            'optimizer': {'values': ['sgd', 'adam', 'rmsprop']},
            'learning_rate': {'values': [0.001, 0.0005, 0.0001, 0.00005]},
            'epochs': {'values': [50]},
            'model_layers': {'values': [[256, 128, 64, 32], [128, 64, 32], [64, 32]]},
            'scheduler': {'values': ['CosineAnnealingLR', 'ReduceLROnPlateau']},
            "batch_size": {'values': [512, 1024, 2048, 4096]},
            "weight_decay": {'values': [0., 1e-5, 1e-3]}

Results

In this LINK hyperparameter tuning with random search results can be observed. The best results were obtained with the following parameters:

Parameter	Value
Learning rate	0.0001
Optimizer	rmsprop
Scheduler	CosineAnnealing
Batch Size	512
Hidden Layers	[256,128,64,32]
Weight Decay	0.00001
Dropout	0.5
Test Loss	0.75
RMSE	0.86

Learning Rate Curvature During Training

Train and Test Losses

Prediction outputs of the model with best weights:

Recommended Top 5 Films for user 1

Toy Story (1995) : Animation|Children's|Comedy

Persuasion (1995) : Romance

Hunchback of Notre Dame, The (1996) : Animation|Children's|Musical

E.T. the Extra-Terrestrial (1982) : Children's|Drama|Fantasy|Sci-Fi

Pleasantville (1998) : Comedy

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