https://github.com/singhvishal003/e-commerce-recommendation-system

Real Time E-commerce recommender system based on user's recent browsing or purchase history.
https://github.com/singhvishal003/e-commerce-recommendation-system

css html jupyter-notebook pandas python surprise

Last synced: about 1 month ago
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Real Time E-commerce recommender system based on user's recent browsing or purchase history.

• Host: GitHub
• URL: https://github.com/singhvishal003/e-commerce-recommendation-system
• Owner: Singhvishal003
• Created: 2025-01-19T08:12:45.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-01-19T08:22:05.000Z (about 1 month ago)
• Last Synced: 2025-01-19T09:26:11.178Z (about 1 month ago)
• Topics: css, html, jupyter-notebook, pandas, python, surprise
• Homepage:
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Real-time Recommendation System for E-commerce

### Project Overview

The Real-time Recommendation System for E-commerce project aims to enhance the shopping experience by providing personalized product recommendations. Using collaborative filtering, this system predicts user preferences based on their previous interactions, making it easier for customers to find products they are likely to purchase.

### Features

- *Collaborative Filtering*: Utilizes collaborative filtering to provide personalized recommendations based on user behavior.

- *Real-time Recommendations*: Generates and displays recommendations in real-time as users interact with the system.

- *Interactive User Interface*: A user-friendly web interface that allows users to input their IDs and receive tailored recommendations instantly.

- *User Feedback*: Integrates a feature for users to rate recommended products, which can be used to refine the recommendation algorithm.

### Project Components

- *Data Collection and Preprocessing*: Involves collecting user interaction data, preprocessing it, and preparing it for model training.

- *Model Training*: Trains a recommendation model using collaborative filtering techniques, specifically the SVD algorithm from the Surprise library.

- *Recommendation Generation*: Provides a function to generate real-time product recommendations based on user input.

- *Web Interface*: A dynamic and visually appealing web interface built with Flask, HTML, and CSS.

### Technical Stack

- *Python*: Main programming language for data preprocessing, model training, and backend development.

- *Flask*: Web framework used to build the interactive user interface.

- *pandas*: Library for data manipulation and analysis.

- *scikit-surprise*: Library for building and evaluating collaborative filtering models.

- *joblib*: Library for model serialization and deserialization.

- *HTML & CSS*: Frontend technologies for building the user interface.

- *Bootstrap*: Framework for responsive design and styling.

### Installation and Setup

1. *Environment Setup*: Create and activate a virtual environment.

2. *Install Dependencies*: Install required Python packages using a requirements file.

3. *Data Preparation*: Load and preprocess user interaction data.

4. *Model Training*: Train the recommendation model using the preprocessed data.

5. *Run the Application*: Launch the Flask application to start the web interface.

### Usage

- *User Input*: Enter a user ID to receive personalized product recommendations.

- *Recommendations*: The system displays a list of recommended products based on the user's previous interactions.

- *Feedback*: Users can rate the recommended products to help improve the recommendation accuracy.

### Future Enhancements

- *Enhanced Algorithms*: Incorporate additional algorithms for improved recommendation accuracy.

- *Expanded Data Sources*: Integrate more diverse data sources to enrich user profiles and preferences.

- *Advanced User Interface*: Develop a more sophisticated and customizable user interface with advanced features like filters and sorting options.

### Conclusion

The Real-time Recommendation System for E-commerce project demonstrates the power of collaborative filtering in creating a personalized shopping experience. By leveraging user interaction data and providing real-time recommendations, this system enhances user engagement and satisfaction, ultimately driving sales and customer loyalty.