leiluk1 / movie-recommender-system

Movie recommendation model on MovieLens dataset

Movie Recommender System

Name: Leila Khaertdinova

Email: [e-mail]

Group number: BS21 DS-02

This project is intended for the second assignment in the Practical Machine Learning and Deep Learning course at Innopolis University.

Task description

A recommender system is a type of information filtering system that suggests items or content to users based on their interests, preferences, or past behavior. These systems are commonly used in various domains, such as e-commerce, entertainment, social media, and online content platforms.

The objective of this assignment is to create a recommender system of movies for users.

Dataset Description

The main dataset used is MovieLens 100K dataset, consisting user ratings to movies. It is already downloaded from here, you can find it in data/raw folder in the repo.

Overview about the dataset:

• It consists of 100,000 ratings from 943 users on 1682 movies;
• Ratings are ranged from 1 to 5;
• Each user has rated at least 20 movies;
• It contains simple demographic info for the users (age, gender, occupation, zip code).

Getting started

To run this project, run the following commands in the repo root directory:

1. Create the virtual environment

   python3 -m venv .venv
   source .venv/bin/activate
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Make sure you have a compatible version of Python 3.9.13 before running the code
4. To check the data exploration and analysis, please check the notebook 1.0-initial-data-exploration in the notebooks directory
5. To check the model training process and results, please see the notebook 2.0-final-solution-train-and-eval in the notebooks directory
6. The evaluation function is placed in benchmark/evaluate.py script
7. To read the final report, check the reports