Forgery-Aware Multi-modal Recommender

Project Overview

This project addresses two critical challenges in the e-commerce industry:

1. Delivering highly personalized and context-aware recommendations.
2. Ensuring product authenticity through advanced forgery detection mechanisms.

The solution integrates a hybrid multimodal recommendation system combining:

• Content-based filtering
• Collaborative filtering
• Context-aware recommendations
• Multimodal recommendations using CLIP (Contrastive Language-Image Pretraining)
• Forgery detection using FD-GAN (Forgery Detection via Generative Adversarial Networks)

By blending recommendation accuracy with authenticity verification, the system aims to redefine the online shopping experience as personalized, secure, and trustworthy.

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Key Features

1. Content-based Filtering:

   • Uses past interactions, likes, and preferences to create a personalized user profile for recommending similar products.

2. Collaborative Filtering:

   • Leverages user behavior patterns to suggest items liked by users with similar interests.

3. Context-aware Recommendations:

   • Factors like location, time, device type, and purchase history refine the recommendations.

4. Multimodal Recommendations (CLIP):

   • Combines textual and visual data for enhanced relevance and accuracy in recommendations.

5. Forgery Detection (FD-GAN):

   • Detects counterfeit product images during seller registration.
   • Reconstructs original images when forgeries are identified, ensuring only authentic products reach users.

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Project Objectives

• Design a hybrid framework incorporating content-based, collaborative, and context-aware personalization techniques.
• Enhance search accuracy and ranking by fusing textual and visual data.
• Develop a robust forgery detection module for verifying product authenticity.
• Improve user satisfaction with personalized, context-sensitive recommendations.

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System Workflow

1. User Interface (UI):

   • Users interact with the platform to search, browse, and purchase products.

2. Data Collection and Processing:

   • User actions are recorded and processed to create detailed profiles and datasets for recommendations.

3. Recommendation Engine:

   • Combines content-based, collaborative, context-aware, and multimodal techniques to deliver tailored suggestions.

4. Forgery Detection Module:

   • Uses FD-GAN to verify the authenticity of product images during seller registration, ensuring only legitimate products are listed.

5. Feedback Loop:

   • Continuously improves recommendations based on user interactions, analytics, and satisfaction metrics.

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Project Requirements

To run this project, ensure you have the following installed:

• Python 3
• Libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • torch (for CLIP and FD-GAN)
  • torchvision