E-Commerce Product Recommendation : Rag Systems

E-Commerce Product Recommendation : Rag Systems - Printable Version

+- Softwarez.Info - Software's World! (https://softwarez.info)
+-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone)
+--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials)
+--- Thread: E-Commerce Product Recommendation : Rag Systems (/Thread-E-Commerce-Product-Recommendation-Rag-Systems--671324)

E-Commerce Product Recommendation : Rag Systems - AD-TEAM - 11-16-2024

[Image: 63814f2162a2046782d923606b24f866.jpg]

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.

[b]What you'll learn[/b]

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

[b]Requirements[/b]

Basic understanding of data analysis concepts

Fundamental understanding of Python programming for data analysis and API interaction

Access to OpenAI API Key

[b]Description[/b]

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 Tongue

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.

[Image: WErBjEXB_o.jpg]