Weighted_Hybrid_Technique_for_Recommender_Systems

Project Overview

This project implements a weighted hybrid technique for recommender systems, combining various recommendation algorithms to improve prediction accuracy and user satisfaction. The approach leverages multiple data sources and recommendation strategies, integrating them in a way that weights their outputs based on their relevance and effectiveness.

Features

• Hybrid Recommendation Model: Combines collaborative filtering, content-based filtering, and other relevant methods into a single recommendation model.
• Weight Customization: Allows for adjustable weights to alter the influence of each component in the final recommendation.
• Performance Metrics: Includes evaluation metrics to assess the accuracy and efficiency of the recommendations.

Requirements

To run this notebook, you will need:

• Python 3.6 or higher
• Libraries: pandas, NumPy, scikit-learn

Installation

Clone this repository to your local machine:

git clone https://github.com/Apoorva-Udupa/Weighted_Hybrid_Technique_for_Recommender_Systems.git
cd weighted-hybrid-recommender

All contributions are welcome.