https://github.com/samueljayasingh/e-commerce-product-recommendation-system
This repository contains a hybrid recommendation system for Amazon products in a CSV File. The system combines collaborative filtering and content-based filtering to provide personalized product recommendations
https://github.com/samueljayasingh/e-commerce-product-recommendation-system
Last synced: 11 months ago
JSON representation
This repository contains a hybrid recommendation system for Amazon products in a CSV File. The system combines collaborative filtering and content-based filtering to provide personalized product recommendations
- Host: GitHub
- URL: https://github.com/samueljayasingh/e-commerce-product-recommendation-system
- Owner: SamuelJayasingh
- License: apache-2.0
- Created: 2025-01-19T20:20:04.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-19T20:31:45.000Z (over 1 year ago)
- Last Synced: 2025-03-16T13:41:43.923Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 1.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# E-Commerce Product Recommendation System
This repository contains a hybrid recommendation system for Amazon products. The system combines collaborative filtering and content-based filtering to provide personalized product recommendations.
## Table of Contents
1. [Introduction](#introduction)
2. [Data Cleaning and Transformation](#data-cleaning-and-transformation)
3. [Collaborative Filtering](#collaborative-filtering)
4. [Singular Value Decomposition (SVD) for Collaborative Filtering](#singular-value-decomposition-svd-for-collaborative-filtering)
5. [Generate Collaborative Filtering Recommendations](#generate-collaborative-filtering-recommendations)
6. [Content-Based Filtering](#content-based-filtering)
7. [Hybrid Recommendation System](#hybrid-recommendation-system)
8. [Real-Time Recommendations](#real-time-recommendations)
9. [Evaluation](#evaluation)
10. [Usage](#usage)
11. [Dependencies](#dependencies)
## Introduction
This recommendation system leverages both collaborative filtering and content-based filtering techniques to provide users with personalized product recommendations. The collaborative filtering component uses user-item interaction data, while the content-based filtering component uses product features to make recommendations.
## Data Cleaning and Transformation
The dataset is first cleaned by removing duplicates and handling missing values. Text fields are standardized by converting to lowercase and removing extra spaces.
## Collaborative Filtering
A User-Item Interaction Matrix is created based on implicit feedback (browsing history or purchase interactions). Interactions are treated as binary values.
## Singular Value Decomposition (SVD) for Collaborative Filtering
Singular Value Decomposition (SVD) is applied to factorize the interaction matrix and generate user-product recommendations based on similarity.
## Generate Collaborative Filtering Recommendations
Recommendations are generated based on the similarity between users.
## Content-Based Filtering
Content-Based Filtering is implemented using TF-IDF vectorization. The `combined_features` column (which includes both category and about_product) is used to compute the similarity between products.
## Hybrid Recommendation System
The hybrid recommendation system combines both collaborative and content-based recommendations.
## Real-Time Recommendations
For real-time updates, you can simulate real-time browsing or interaction and immediately generate recommendations based on the recent activity.
## Evaluation
The recommendation system can be evaluated using Precision, Recall, or Mean Average Precision (MAP) by comparing the generated recommendations with actual user preferences.
## Usage
To use the recommendation system, follow these steps:
1. Load the dataset and clean the data.
2. Create the User-Item Interaction Matrix.
3. Apply SVD for dimensionality reduction.
4. Generate collaborative filtering recommendations.
5. Implement content-based filtering using TF-IDF vectorization.
6. Combine collaborative and content-based recommendations to form a hybrid recommendation system.
7. Generate real-time recommendations based on recent user activity.
8. Evaluate the recommendation system using appropriate metrics.
## Dependencies
The following Python libraries are required:
- pandas
- numpy
- scikit-learn
You can install these dependencies using pip:
```sh
pip install pandas numpy scikit-learn
```
To install the dependencies listed in the `requirements.txt` file, you can use the following command:
```sh
pip install -r requirements.txt
```
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Disclaimer
⚠️ This project is solely for educational purposes to acquire knowledge about Collaborative Filtering and Singular Value Decomposition (SVD) for Collaborative Filtering.