DealDay Recommendation System

Welcome to the DealDay Recommendation System repository. This project aims to demonstrate the implementation of AI and data-driven recommendation systems for DealDay, an innovative e-commerce platform in Nigeria. While DealDay's proprietary data is not available for this proof of concept, we have strategically utilized open-source data from Amazon and Home Depot to simulate real-world scenarios. Our goal is to showcase how AI-driven recommendation systems can enhance user experiences and boost sales on the DealDay platform.

Table of Contents

• Introduction
• Methods
  • Product Popularity Based Recommendation System
  • Model-Based Collaborative Filtering System
  • Text-Based Clustering Recommendation System
• Deployment Strategy and Implementation
• Contributing
• License

Introduction

DealDay's success as a leading e-commerce platform in Nigeria relies on offering customers personalized and relevant product recommendations. To achieve this, we have explored and implemented three distinct recommendation systems:

Methods

Product Popularity Based Recommendation System

Method: This approach analyzes user ratings to identify popular products and tailor recommendations based on overall popularity.

Advantages:

• Quickly identifies products with high customer engagement.
• Tailors recommendations based on overall popularity.
• Particularly helpful to new customers who have no purchase history.

Model-Based Collaborative Filtering System

Method: Utilizing collaborative filtering and Singular Value Decomposition (SVD) to establish user-product correlations.

Advantages:

• Provides personalized recommendations by finding hidden patterns.
• Offers suggestions based on user interactions and preferences.
• Effective even for new users with limited purchase history.

Text-Based Clustering Recommendation System

Method: This system clusters products using TF-IDF and K-Means based on textual descriptions.

Advantages:

• Considers contextual relevance through textual information such as product descriptions.
• Offers targeted recommendations based on product characteristics rather than popularity.
• Enhances user engagement and discovery of relevant deals.

Deployment Strategy and Implementation

Integrating these recommendation methods into DealDay's platform harnesses the power of AI and data-driven insights. Our deployment strategy includes data collection, model implementation, product integration, and continuous feedback for improvement. By embracing AI and data-driven insights, DealDay aims to enhance the customer shopping experience and solidify its position as one of the leading e-commerce platforms in Nigeria.

Contributing

We welcome contributions from the open-source community. If you'd like to contribute to this project or have any suggestions, please feel free to open an issue or create a pull request.

License

This project is licensed under the MIT License.