E-commerce Product Recommendation System

Project Overview

This project implements a comprehensive product recommendation system for an e-commerce platform. The system utilizes multiple recommendation techniques including collaborative filtering, content-based filtering, hybrid approach, and an advanced multi-modal deep learning model to provide highly personalized product recommendations to users based on their purchase and browsing history.

Features

• Collaborative Filtering: Recommends products based on the purchase behavior of similar users.
• Content-Based Filtering: Recommends products based on the attributes of the products the user has previously interacted with.
• Hybrid Approach: Combines collaborative and content-based filtering for more robust recommendations.
• Multi-Modal Deep Learning: Leverages product images, text descriptions, and user-item interactions using a sophisticated neural network architecture.
• User-Friendly Interface: A web application built with Flask for easy interaction and visualization of recommendations.

Technologies Used

• Python: For backend logic and data processing.
• PyTorch: For building and training the multi-modal neural network model.
• Flask: Web framework for creating the web application.
• Pandas: For data manipulation and analysis.
• Sentence Transformers: For natural language processing of product descriptions.
• PyTorch Geometric: For graph-based neural network components.
• ResNet50: For image feature extraction.
• HTML/CSS: For frontend design.

System Architecture

The recommendation system consists of four main components:

1. Collaborative Filtering Module: Analyzes user purchase patterns to find similar users and recommend products they've purchased.

2. Content-Based Filtering Module: Analyzes product attributes (especially categories) and user browsing history to recommend similar products.

3. Hybrid Recommendation Module: Combines results from both collaborative and content-based approaches, with fallback to popular products when necessary.

4. Multi-Modal Deep Learning Model: A sophisticated neural network that processes:

   • User embeddings
   • Product embeddings
   • Image features (using ResNet50 with fashion-specific optimizations)
   • Text features (using Sentence Transformers)
   • Graph-based relationships (using Graph Convolutional Networks)

Getting Started

Prerequisites

• Python 3.x
• PyTorch
• Flask
• Pandas
• Sentence Transformers
• PyTorch Geometric
• Torchvision
• PIL

Installation

1. Clone the repository:

   git clone <repository-url>

2. Navigate to the project directory:

   cd er

3. Install the required packages:

   pip install -r requirements.txt

Dataset Setup

1. Download the DeepFashion dataset from official website

2. Create a datasets directory in the project root:

   mkdir datasets

3. Extract the downloaded dataset into the datasets directory. The directory structure should look like:

   datasets/
     ├── shape_ann/
     ├── test_images/
     └── train_images/
   

4. Place the following data files in the project root:

   • users_expanded.csv
   • products_expanded.csv
   • product_images_expanded.csv
   • purchases_expanded.csv
   • browsing_history_expanded.csv

Note: The dataset files are not included in the repository due to their large size. Please download them separately.

Running The Application

1. Start the Flask server:

   python app.py

2. Open your web browser and navigate to http://127.0.0.1:5000.

Data Files

1. users_expanded.csv: Contains user information.
2. products_expanded.csv: Contains product details such as name, category, description, price, and rating.
3. product_images_expanded.csv: Contains mappings between products and their image files.
4. purchases_expanded.csv: Contains user purchase history.
5. browsing_history_expanded.csv: Contains user browsing history.

Dataset

The system uses fashion product images from the DeepFashion dataset, which includes various clothing items with multiple view angles (front, side, back, full). The multi-modal model is specifically optimized for fashion recommendations with view-aware processing.

Usage

1. Enter a user ID and select a recommendation method (collaborative, content-based, hybrid, or multi-modal).
2. Click on "Get Recommendations" to view the recommended products.
3. The system will display both products the user has interacted with and new recommendations.

Multi-Modal Model Details

The multi-modal model (MultiModalModel class) integrates multiple data sources:

• Collaborative Filtering Component: User and product embeddings.
• Image Processing Component: Fashion-optimized ResNet50 with view-type awareness (front, side, back, full views).
• Text Processing Component: Sentence transformer to encode product descriptions.
• Graph Component: Graph convolutional network for modeling product relationships.
• Fusion Layer: Combines all modalities for final recommendation scores.

Screenshots

Contributing

If you would like to contribute to this project, please fork the repository and submit a pull request.

Future Enhancements

• More Data: Plans to expand the dataset to enhance the model's recommendation capabilities.

• Clean UI: Improving the user interface (UI) to enhance user experience and engagement.

• Feedback Mechanism: Implement a way for users to provide feedback on the recommendations, which can help refine the system.

• Explainability: Work on explaining the reasoning behind recommendations, which can enhance user trust and satisfaction.

• Model Optimization: Fine-tune the multi-modal model for better performance and faster inference.

• Additional Modalities: Incorporate more data sources such as user demographics and seasonal trends.