MovieMatcher: Movie Recommender System using MovieLens Dataset

Overview

MovieMatcher is a movie recommendation system developed using the MovieLens dataset, which contains over 32 million user ratings. This project focuses on building a scalable and accurate recommendation pipeline by leveraging advanced techniques such as Collaborative Filtering, Matrix Factorization, Content-Based Filtering, and Hybrid Models. The goal is to address common challenges like data sparsity, cold start problems, and over-specialization.

Key Contributions

• Designed and implemented a scalable recommendation pipeline, from data ingestion to model deployment.
• Conducted Exploratory Data Analysis (EDA) and Feature Engineering to uncover meaningful patterns.
• Developed models using techniques such as Singular Value Decomposition (SVD) and hybrid approaches to enhance recommendation diversity and accuracy.
• Achieved a Root Mean Square Error (RMSE) of 0.9478 and a Mean Absolute Error (MAE) of 0.7366, ensuring high prediction accuracy.
• Ensured 100% user-item coverage and maintained a recommendation diversity of 0.58, preventing over-specialization.

Technologies & Tools

• Python
• Pandas
• NumPy
• Scikit-learn
• Surprise Library
• Matplotlib
• Seaborn

How to Use

1. Download the Dataset
   The dataset used in this project can be downloaded from the MovieLens 32M Dataset.

2. Clone the Repository
   To clone the repository, use the following command:

   git clone https://github.com/abhipatel35/MovieMatcher-Movie-Recommender-System
   
   

3. Install Required Libraries
   Make sure to install the required libraries.

4. Run the Jupyter Notebook
   After downloading the dataset and installing the required libraries, open the moviematcher.ipynb Jupyter notebook to explore and run the code.

Impact

• Demonstrated effective methods to integrate both explicit and implicit feedback, making the system more inclusive and personalized.
• Highlighted the potential to scale and apply these methodologies beyond the entertainment domain, such as in e-commerce and online education.

Skills Demonstrated

• Machine Learning
• Data Analysis
• Recommender Systems
• Python Programming
• ML Models
• Collaborative Filtering

License

This project is licensed under the MIT License - see the LICENSE file for details.