MoviesRecommender

This repository contains utilities that provide the ability to generate a visual content-based full-length movie dataset.

☑️ Prerequisites

In order to run the application, you will need to install the Python libraries listed below:

• Python >= 3.4
• NumPy >= 1.19
• SciPy >= 1.6
• PyInquirer >= 1.0.3
• OpenCV-Python >= 4.1.1
• Tensorflow >= 2.6.0
• Keras >= 2.6.0
• CUDA >= 11.4

For a simple solution, you can simply run the below command in the root directory:

pip install -r requirements.txt

Note that you should also install NVIDIA® CUDA® Deep Neural Network library™ (cuDNN) for high-performance GPU acceleration. This is used when training the DNN models in the feature extraction stage.

📑 Files and Modules

The application contains two main modules, including Data Processing, Frame Extraction, and Visual Feature Extraction, all accessible via the main.py file. Each of the mentioned modules provide a different set of utilities:

I. Data Processing

Contains tools for generating the movies dataset and analyzing its contents. You can find a detailed explanation about each functionalities below:

A) New Dataset Generator

This module requires a CSV file containing the movies metadata. The mentioned metadata includes movieId, title, and genre. The most important hint here is that the items of these fields must match MovieLenz-25M dataset instances. Here you can find a simple input file:

movieId	title	genre
1203	12 Angry Men (1957)	Crime, Drama
79132	Inception (2010)	Action, Crime, Drama, Mystery
...	...	...

Using the datasetGenerator.py file of this module, movieIds will merge with the MovieLenz metadata and generates a more detailed dataset. The mentioned fields include userIds rated for the movie, IMDB rating, and timestamp of rating.

B) Data Analyzer

As you can guess, this module provides some statistics from the dataset, including number of movies, distribution of genres in a chart, total number of ratings and interacted users, etc.

🚀 Launch the Application

After installing required packages, the first step to run the engine of the application is to provide a proper configuration file. The main.py file, which is the start point of the application, needs such file to provide customized configurations for its differernt modules and then run the application:

I. Make a Configuration File

You can find a config.example.py in the root directory. What you need to do is to make a copy of this file and rename it to config.py. There, you can apply your customized settings:

import os

# Dataset Generation
moviesListCSV = 'C:/Some/Path/Dataset.csv'
generatedListCSV = os.path.abspath('./Data/generated/output.csv')
movieLenzRatings = 'C:/Some/Path/ratings.csv'

# Feature Extraction
networkInputSize = 420
moviesDir = 'C:/Some/Path/'
framesDir = 'C:/Some/Path/'

As you can see, you need to specify some paths and settings for both Dataset Generation and Feature Extraction modules:

• moviesListCSV: The path to the dataset.csv file that contains data of the collected movies
• generatedListCSV: The path where the output dataset should be generated
• movieLenzRatings: The path to the MovieLenz ratings file
• moviesDir: The directory that contains a set of movie files
• framesDir: The folder to hold directories of the extracted frames
• networkInputSize: The width of the extracted frames (height will be calculated by the system based on video's aspect-ratio)

Please note that the config.py is placed in .gitignore file due to the customized settings.

II. Run the Application

After providing the configurations, you can easily run the app using the command below in the root directory:

python ./main.py

TODOs

• [feat] Run aggregation (mean, max, gmm, min) on Shots and Movies