https://github.com/saniyaabushakimova/movie-recommender-system

An interactive Movie Recommender System built from scratch using collaborative filtering. It includes a Dash web app where users can rate movies and receive personalized recommendations in real-time. Tech: Python (numpy, pandas, dash, )
https://github.com/saniyaabushakimova/movie-recommender-system

collaborative-filtering cosine-similarity dash icbf movie-recommendation-app python

Last synced: 2 months ago
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An interactive Movie Recommender System built from scratch using collaborative filtering. It includes a Dash web app where users can rate movies and receive personalized recommendations in real-time. Tech: Python (numpy, pandas, dash, )

• Host: GitHub
• URL: https://github.com/saniyaabushakimova/movie-recommender-system
• Owner: SaniyaAbushakimova
• Created: 2025-02-25T18:05:00.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-02-26T15:52:04.000Z (9 months ago)
• Last Synced: 2025-03-05T18:49:14.686Z (8 months ago)
• Topics: collaborative-filtering, cosine-similarity, dash, icbf, movie-recommendation-app, python
• Language: Jupyter Notebook
• Homepage:
• Size: 60.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Movie-Recommender-System

Project completed on December 9, 2024.

## Project Overview

This project is a Movie Recommender System that leverages **collaborative filtering techniques** to generate personalized movie recommendations. It is built using the **MovieLens dataset**, which includes approximately **1 million ratings** from **6,040 users** for **3,706 movies**, providing a rich foundation for analyzing user preferences and movie similarities.

The recommender system is implemented from scratch, focusing on two key recommendation strategies:

1. **System 1: Popularity-Based Recommendation** – Recommends movies based on overall popularity.

2. **System 2: Item-Based Collaborative Filtering (IBCF)** – Uses similarity between movies to generate personalized recommendations for users.

Additionally, a **Movie Recommender Web App** was built to provide an interactive interface for users to explore and receive movie recommendations.

See Movie Recommender Web App demo below:

https://github.com/user-attachments/assets/f8afa8af-f9bc-43cc-a69f-87c63ba60f40

## Repository Contents

* `Movie-Recommender-App/` - Folder containing the Dash web application that allows users to explore movie recommendations interactively.

  * `app.py` - The main Dash application that serves the web interface for recommendations.

  * `myfuns.py` - Contains helper functions for generating recommendations based on precomputed similarity scores.

  * `popular_movies.csv` - A dataset containing the most popular movies based on rating frequency and scores.

  * `S_top_30.csv` - A dataset containing similarity scores for the top 30 most similar movies for each title.

  * `requirements.txt` - A list of required Python libraries to run the Dash app.

* `MovieImages/` - Folder containing movie posters used in the recommendations web app.

* `ml-1m/` - Folder containing the MovieLens 1M dataset, which includes user ratings, movie metadata, and user demographic information.

  * `movies.dat` - Includes movie title, release year, and genres.

  * `ratings.dat` - 1,000,209 anonymous ratings from 6,040 users on 3,706 movies.

  * `users.dat` -  Gender, age, occupation, and zip code.

* `Instructions.pdf` - Project details and problem statement.

* `Movie-Recommender-System.ipynb` - Jupyter Notebook containing the implementation of the recommender system, including data preprocessing, model development, and evaluation.

* `Rmat.csv` - User-movie rating matrix generated from the MovieLens dataset used as the input for IBCF.

* `top_10_movies.csv` - A list of the top 10 most recommended movies for each user based on Popularity-Based Recommendation.

* `similarity_matrix.csv` - Precomputed similarity scores between movies, used for IBCF. [Download here](https://drive.google.com/file/d/142abx3DEtxWVGX4cVKAKlrKbF1JRJLP0/view?usp=sharing)

## Methods and Techniques Used

1. **Data Preprocessing**

 * Constructed a user-movie rating matrix from raw MovieLens ratings.

 * Normalized ratings by centering each row to adjust for user rating biases.

 * Removed movies with very few ratings to improve model efficiency.

2. **Popularity-Based Recommendation**

 * Ranked movies based on the number of user ratings, considering only those with an average rating above 4.3.

 * Recommended the top 10 most popular movies to all users.

3. **Item-Based Collaborative Filtering (IBCF)**

 * Computed a movie similarity matrix using cosine similarity.

 * Only retained the top 30 most similar movies for each title to enhance efficiency.

 * Used similarity scores to generate personalized recommendations for users based on their watched movies.

4. **Web Application**

 * Developed an interactive Dash web app where users can rate movies and receive recommendations.

 * The app leverages precomputed similarity scores for fast, real-time recommendations.

## How to Run the Web App

1. **Install Dependencies:**

   

`pip install -r requirements.txt`

2. **Run the Dash App:**

   

`python app.py`