Enhancing Personalized Product Recommendations in E-Commerce Using Multimodal and Session-Based User Modeling

A system that enhances product recommendations in e-commerce using multimodal and session-based user modelling.

A system that enhances product recommendations in e-commerce using multimodal and session-based user modelling. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners building full-stack skills: user accounts and roles, a database-backed back end, a responsive front end and deployment.

The main parts are personalised recommendations ranked by relevance, an order and payment flow, fusion of more than one type of input, secure sign-up with role-based access, and a database-backed back end with a clear API. The reference build uses scikit-learn, Razorpay, React, Node.js, and Express, with a clear project structure, setup instructions and sample data so that it can be run and extended.