🎬 Movie Recommendation System using CognoDB

📌 Project Overview

The Movie Recommendation System is a graph-based web application developed using Python, Flask, and CognoDB. The application stores movies, users, genres, actors, and directors as graph nodes and uses graph traversal to recommend movies based on user interests.

Unlike traditional SQL databases, CognoDB efficiently manages highly connected data using nodes and relationships, making it ideal for recommendation systems.

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Features

• View All Movies
• Search Movies
• View Movie Details
• Add New Movie
• Edit Movie
• Delete Movie
• Personalized Movie Recommendations
• Graph Database Traversal
• Responsive Bootstrap User Interface
• REST API Support

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Technologies Used

Backend

• Python 3
• Flask

Database

• CognoDB
• Neo4j Python Driver

Frontend

• HTML5
• CSS3
• Bootstrap 5
• JavaScript

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Project Structure

MovieRecommendationSystem/
│
├── queries/
│   ├── movie_queries.py
│   └── recommendation_queries.py
│
├── routes/
│   ├── movie_routes.py
│   └── recommendation_routes.py
│
├── services/
│   ├── movie_service.py
│   └── recommendation_service.py
│
├── templates/
│
├── static/
│   ├── css/
│   ├── js/
│   └── images/
│
├── tests/
│
├── app.py
├── config.py
├── database.py
├── seed.py
├── requirements.txt
├── README.md
├── GRAPH_MODEL.md
├── PROJECT_REPORT.md
└── .gitignore

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Why a Graph Database?

Traditional relational databases store data in tables and retrieve related information using JOIN operations.

This project contains highly connected entities such as:

• Users
• Movies
• Genres
• Actors
• Directors

Graph databases like CognoDB store these relationships directly, making recommendation queries much faster and easier than multiple SQL JOIN operations.

Example relationships:

User ------LIKES------> Movie

Movie -----BELONGS_TO-----> Genre

Movie -----DIRECTED_BY-----> Director

Movie -----ACTED_BY-----> Actor

The recommendation engine traverses these relationships to suggest movies based on the genres of movies liked by a user.

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Graph Data Model

                +-----------+
                |   User    |
                +-----------+
                     |
                   LIKES
                     |
                     ▼
                +-----------+
                |   Movie   |
                +-----------+
                 /    |    \
                /     |     \
               ▼      ▼      ▼
           Genre   Actor  Director

Nodes

• User
• Movie
• Genre
• Actor
• Director

Relationships

• LIKES
• BELONGS_TO
• ACTED_BY
• DIRECTED_BY

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Main Cypher Queries

Get All Movies

MATCH (m:Movie)
RETURN m;

---

Get Movie By ID

MATCH (m:Movie {id:$id})
RETURN m;

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Search Movie

MATCH (m:Movie)
WHERE toLower(m.title) CONTAINS toLower($title)
RETURN m;

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Recommendation Query

MATCH (u:User {name:$user})-[:LIKES]->(:Movie)-[:BELONGS_TO]->(g:Genre)

MATCH (m:Movie)-[:BELONGS_TO]->(g)

WHERE NOT (u)-[:LIKES]->(m)

RETURN DISTINCT
m.title,
m.rating
ORDER BY m.rating DESC;

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Movies By Director

MATCH (m:Movie)-[:DIRECTED_BY]->(d:Director)
RETURN m,d;

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Movies By Actor

MATCH (m:Movie)-[:ACTED_BY]->(a:Actor)
RETURN m,a;

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Movies By Genre

MATCH (m:Movie)-[:BELONGS_TO]->(g:Genre)
RETURN m,g;

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REST APIs

Get All Movies

GET /movies

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Get Movie By ID

GET /movies/<id>

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Search Movie

GET /search?title=MovieName

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Add Movie

POST /add-movie

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Update Movie

POST /edit-movie/<id>

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Delete Movie

GET /delete-movie/<id>

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Recommendation

GET /recommend/Aravind

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Installation

Clone the repository

git clone https://github.com/Diyash-18/MovieRecommendationSystem.git

Move into the project folder

cd MovieRecommendationSystem

Create a virtual environment

python -m venv venv

Activate virtual environment

Windows

venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

Seed the database

python seed.py

Run the application

python app.py

Open in browser

http://127.0.0.1:5000

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Testing

Database Connection

python tests/test_connection.py

Verify Database

python tests/verify_db.py

Recommendation Test

python tests/test_recommendation.py

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Project Features

• Graph Database Implementation
• Movie CRUD Operations
• Search Functionality
• Recommendation Engine
• Multi-Hop Graph Traversal
• Parameterized Cypher Queries
• Responsive Bootstrap UI
• REST API Integration
• Modular Flask Architecture

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Future Enhancements

• User Authentication
• Login & Registration
• Movie Posters API
• User Ratings
• Watchlist Feature
• AI-Based Recommendations
• Admin Dashboard
• Collaborative Filtering
• Movie Reviews

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Live Demo

https://movie-recommendation-system-ifn3.onrender.com

Developed By

DIYA

Movie Recommendation System

Developed using Python, Flask, and CognoDB as part of the CognoDB Assignment.