E-Commerce Product Recommendation : RAG Systems

Name: E-Commerce Product Recommendation : RAG Systems
Rating: 3.8 (3 reviews)

Get hands-on with a practical Generative AI course.

Created byAhmad Varasteh

Last updated 10/2024

English

What you'll learn

Use OpenAI APIs to perform context-based searches efficiently and effectively.
Prepare vector data for a Retrieval-Augmented Generation (RAG) system using OpenAI's text embedding models.
Implement cosine similarity to enhance recommendation systems by identifying and understanding data relationships and patterns.
Craft context-reach prompt for a user-friendly product recommendation system

Course content

1 section • 7 lectures • 38m total length

Set up the project environment6:11
In this task, we will set up the Google Colab environment, install and import the necessary Python modules, and configure the OpenAI API.
Prepare the knowledge base9:51
In this task, we will load the product dataset and review its contents. We will then generate the text embedding vectors required for our RAG system.
Retrieve relevant items based on user prompts8:21
In this task, we will use the embedding vectors to retrieve relevant items based on the user's search phrase.
Practice Task3:08
You have been hired by a 40-year-old news company called FF-NEWS. They have provided you with a list of their news headlines from March 2004. They are seeking 10 headlines from their newspaper specifically related to climate change and global warming.
As an AI Engineer, your task is to use the OpenAI text embedding model to identify 10 headlines about climate change and global warming from their archive.
Craft the context-reach prompt3:42
In this task, we use the retrieved products to craft a context-rich prompt for our LLM request.
Prompt the LLM to generate product recommendation4:34
In this task, we will prompt the LLM using the context-rich prompt we created to generate user-friendly product recommendations based on the user's search phrase.
Challenge Task2:29
You have been hired by a 40-year-old news company called FF-NEWS. They have provided a dataset containing headlines, their vector embeddings, and similarity scores to the phrase climate change and global warming.
As an AI Engineer, your task is to use the OpenAI GPT-4 model to generate a short article based on the top 3 most similar headlines to climate change and global warming.

Requirements

Basic understanding of data analysis concepts
Fundamental understanding of Python programming for data analysis and API interaction
Access to OpenAI API Key

Description

By the end of this project, you will be equipped to perform context-based searches using Retrieval-Augmented Generation (RAG) systems and the OpenAI API, as well as develop a personalized recommendation system. You’ve been hired by ShopVista, a leading e-commerce platform offering products ranging from electronics to home goods. Your goal is to improve the platform's product recommendation system by creating a context-driven search feature that delivers tailored suggestions based on users' search phrases. You'll work with a dataset of product titles, descriptions, and identifiers to build a recommendation system that enhances the shopping experience.

Learning Objectives:

Prepare vector data for a Retrieval-Augmented Generation (RAG) system using OpenAI's text embedding models.
Implement cosine similarity to identify and understand data relationships and patterns, improving recommendation systems.
Utilize OpenAI APIs to perform efficient and effective context-based searches.
Design and develop context-rich prompts for a user-friendly product recommendation system.

This project will provide you with a comprehensive understanding of AI-powered search and recommendation systems, enabling you to grasp how cutting-edge technologies such as Retrieval-Augmented Generation (RAG) and OpenAI’s models can be applied to solve real-world challenges. As you work through the project, you'll learn how to prepare and manage large datasets, leverage advanced text embedding techniques, and use AI to improve user interactions with e-commerce platforms.

By implementing context-based searches and personalized recommendation features, you'll enhance your technical capabilities in areas such as natural language processing, vector-based data retrieval, and algorithm development. Furthermore, the practical experience gained from building a recommendation system for a leading e-commerce platform like ShopVista will deepen your problem-solving skills, allowing you to address complex customer needs with AI-driven solutions. This hands-on experience will not only strengthen your expertise in the e-commerce domain but also broaden your ability to design user-centric applications that deliver personalized, relevant, and intuitive experiences.

Who this course is for:

Data scientists who are looking for more hands-on practice with RAG systems and Generative AI.