E-Commerce Product Recommendation : Rag Systems
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 296.53 MB | Duration: 0h 38m
Get hands-on with a practical Generative AI course.
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
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
repare 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. Overview
Section 1: Introduction
Lecture 1 Set up the project environment
Lecture 2 Prepare the knowledge base
Lecture 3 Retrieve relevant items based on user prompts
Lecture 4 Practice Task
Lecture 5 Craft the context-reach prompt
Lecture 6 Prompt the LLM to generate product recommendation
Lecture 7 Challenge Task
Data scientists who are looking for more hands-on practice with RAG systems and Generative AI.
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