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Product Recommender System

https://directfind.onrender.com

Overview

This is a Django-based product recommendation system designed to manage and display product data. The application imports product information from CSV files (specifically Amazon product data) and stores them in a database for use in a recommendation engine. The project is in early development stages with the data model and import functionality established, but the recommendation logic and user-facing views are yet to be implemented.

User Preferences

Preferred communication style: Simple, everyday language.

System Architecture

Framework and Structure

  • Framework: Django 5.0 with a standard project layout
  • Main Project: django_project/ contains settings, URLs, and WSGI/ASGI configurations
  • App: recommender/ is the core application handling product data and recommendations

Data Model

The system uses a single Product model with the following fields:

  • product_id: Unique identifier for products
  • product_name: Name/title of the product
  • category: Product category classification
  • discounted_price: Price stored as string (allows currency symbols)
  • about_product: Product description
  • img_link: URL to product image

Data Import

  • import_data.py handles CSV imports using pandas
  • Uses Django's update_or_create for idempotent imports
  • Source data comes from CSV files in attached_assets/ directory

Current State

  • Database migrations are set up
  • Product model is defined
  • CSV import utility is functional
  • Views and URL routing for the recommender app are not yet implemented
  • No recommendation algorithm implemented yet

External Dependencies

Python Packages

  • Django 5.0+: Web framework
  • pandas: CSV data processing for imports

Database

  • Uses Django's default SQLite database (no explicit database configuration visible)
  • May need PostgreSQL for production scaling

Data Sources

  • Amazon product CSV files stored in attached_assets/ directory
  • Expected CSV columns: product_id, product_name, category, discounted_price, about_product, img_link

About

E- Commerce Recommendation system

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