Movie Recommendation System Using Auto Encoders

Abstract

1. Introduction

Movie Recommendation System built using AutoEncoders.It was trained on MovieLens Dataset.It follows collaborative filtering method. The Collaborative Filtering Recommender is entirely based on the past behavior and not on the context. More specifically, it is based on the similarity in preferences, tastes and choices of two users. It analyses how similar the tastes of one user is to another and makes recommendations on the basis of that.

The dataset that I’m working with is MovieLens, one of the most common datasets that is available on the internet for building a Recommender System. The version of the dataset that I’m working with (1M) contains 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000

An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner.The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction. Recently, the autoencoder concept has become more widely used for learning generative models of data.Some of the most powerful AI in the 2010s have involved sparse autoencoders stacked inside of deep neural networks

2. Objective

Built a Movie Recommendation System using AutoEncoders.It was built using MovieLens Dataset

This project demonstrates how Python, PyTorch can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/SudharshanShanmugasundaram/Movie-Recommendation-System-using-AutoEncoders.git
cd Movie-Recommendation-System-using-AutoEncoders

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by SudharshanShanmugasundaram and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.