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所在平台: Udemy |
课程主页: https://www.udemy.com/course/e-commerce-product-recommendation-rag-systems/
课程评论：没有评论
课程名称：电子商务产品推荐：RAG系统 课程概述：该项目旨在掌握如何使用增强检索生成（RAG）系统和OpenAI API进行基于上下文的搜索，并开发个性化推荐系统。您将受雇于ShopVista，一家提供电子产品到家居用品等多种商品的领先电子商务平台。您的目标是通过创建一个基于上下文的搜索功能，提升平台的产品推荐系统，以便根据用户的搜索词提供量身定制的建议。您将使用产品标题、描述和标识符的数据集来构建推荐系统，从而改善购物体验。 学习目标： 1. 准备向量数据，以便使用OpenAI的文本嵌入模型进行增强检索生成（RAG）系统。 2. 实施余弦相似度，以识别和理解数据之间的关系和模式，从而提升推荐系统的效果。 3. 利用OpenAI API执行高效的上下文搜索。 4. 设计和开发具有上下文丰富的提示，以便创建用户友好的产品推荐系统。 通过该项目，您将全面了解基于人工智能的搜索和推荐系统，学习如何将先进的技术（如增强检索生成（RAG）和OpenAI模型）应用于解决现实世界中的挑战。在实践过程中，您将学习如何准备和管理大型数据集，利用先进的文本嵌入技术，并使用人工智能改善用户与电子商务平台的互动。 通过实施基于上下文的搜索和个性化推荐功能，您将增强在自然语言处理、基于向量的数据检索和算法开发等领域的技术能力。此外，在为ShopVista构建推荐系统的实践经验将加深您解决问题的能力，使您能够用AI驱动的解决方案来满足复杂的客户需求。这种实践经验不仅会增强您在电子商务领域的专业知识，还会拓宽您设计用户中心应用的能力，以提供个性化、相关且直观的用户体验。
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.
