🎥 Recommendation Engine Project

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📋 Overview

The Recommendation Engine Project is a Streamlit-based web application that demonstrates the implementation of content-based filtering for recommendation systems. It showcases two key applications:

• 🎥 Movie Recommender System: Recommends movies based on metadata such as genres, cast, and directors.
• 🌍 Job Recommender System: Recommends jobs by analyzing textual features like job descriptions and titles.

This project highlights the power of data-driven recommendations and provides an interactive way to explore content-based recommendation systems.

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Table of Contents

1. 🎯 Objectives
2. 🔧 Technologies Used
3. 🗂️ Directory Structure
4. 📁 Features
5. 🔄 Project Workflow
6. 🎉 Conclusion
7. 📚 References
8. 📜 License

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🎯 Objectives

• Build an interactive platform to demonstrate content-based filtering in real-world applications.
• Provide a hands-on experience for users to explore recommendation results dynamically.
• Use metadata and textual data effectively for generating recommendations.

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🔧 Technologies Used

Other libraries:

• Pandas
• Scikit-learn
• Numpy

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🗂️ Directory Structure

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├── LICENSE
├── README.md
├── app
│   ├── 1_Introduction_🎉.py
│   ├── component.py
│   ├── pages
│       ├── 2_Movies_Recommendation_System_🍿.py
│       └── 3_Jobs_Recommendations_System_🌍.py
├── artifacts
│   ├── movie_list.pkl
│   └── similarity.pkl.gz
├── assets
│   ├── Job_Recommender_Background.png
│   ├── movie_background.jpg
├── datasets
│   ├── tmdb_5000_credits.csv
│   ├── tmdb_5000_movies.csv
│   ├── Synthetic_Job_Postings_Data.csv
│   └── Synthetic_Resumes_Data.csv
└── requirements.txt

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📁 Features

1. 🎥 Movie Recommender System

• Uses content-based filtering.
• Recommends movies by analyzing metadata (e.g., genres, directors, cast).
• Allows users to select a movie and receive similar recommendations.

2. 🌍 Job Recommender System

• Based on TF-IDF vectorization of job titles and descriptions.
• Provides job recommendations tailored to user input.

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🔄 Project Workflow

1. 📂 Environment Setup:

   • Install dependencies using requirements.txt.

2. 🔍 Data Processing:

   • Process datasets (TMDb movies, synthetic job postings) for feature extraction.

3. 🧠 Model Development:

   • Create similarity matrices using content-based techniques like cosine similarity and TF-IDF.

4. 🚀 Web App Development:

   • Build dynamic dashboards using Streamlit for both recommendation systems.

5. 🌐 Deployment:

   • Deploy the app using Streamlit's cloud services.

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🎉 Conclusion

This project demonstrates how content-based filtering can be applied effectively in different domains like movies and jobs. The interactive application enables users to explore recommendations dynamically and learn about the underlying algorithms.

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📚 References

• Streamlit Documentation
• TMDb API Documentation
• Scikit-learn Documentation

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📜 License

Fahmi Zainal Custom License
Unauthorized copying, distribution, or modification of this project is prohibited. For inquiries, contact the project owner.