🎯 Recommender System

This project implements a movie recommender system using collaborative filtering techniques. It includes both memory-based and model-based approaches, namely user-based k-Nearest Neighbors (k-NN) and Matrix Factorization using Stochastic Gradient Descent (SGD). The system reads user rating data from CSV files, predicts ratings, and generates personalized top-N recommendations.

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🏷️ Badges

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💡 Key Features

• 📥 Load user-item rating data from CSV files
• 📈 Predict unseen ratings using:
  • User-user collaborative filtering (k-NN + cosine similarity)
  • Matrix factorization (latent factor model)
• 🧠 Generate personalized top-N recommendations
• 📊 Evaluate model accuracy using RMSE
• 🧱 Modular and extendable Python codebase

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📁 Dataset Format

The system expects a CSV file with the following structure:

user_id	item_id	rating	timestamp
1	101	4.0	874965758

Example datasets: MovieLens 100K, custom CSV files, etc.

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🧪 Evaluation

The system uses Root Mean Squared Error (RMSE) to evaluate the prediction performance on a test/validation split.

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▶️ How to Run

1. Clone the repository:

git clone https://github.com/your-username/recommender-system.git
cd recommender-system

📌 To Use:

• Save this as your README.md in your root folder.
• Replace any placeholder GitHub link or path with your actual usernames and structure if needed.