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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/recommender-systems-introduction
课程评论：没有评论
课程名称：推荐系统简介：非个性化与基于内容的推荐 课程概述：本课程是推荐系统专业的第一门课程，旨在介绍推荐系统的概念，详细回顾多个实例，并引导学员通过摘要统计和产品关联进行非个性化推荐，掌握基本的基于刻板印象或人口统计的推荐以及基于内容的过滤推荐。在完成本课程后，学员将能够使用基础电子表格工具从数据集中计算多种推荐，如果完成荣誉追踪，还将使用开源的LensKit推荐工具包编程实现这些推荐。 除了详细的讲解和互动练习，课程还包含了对几位在推荐系统研究和实践领域的领导者进行的访谈，讨论高级主题和当前方向。 课程大纲： 1. 课程前言：简要介绍推荐系统主题（包括将该技术置于历史背景中）并提供课程和专业的结构概述。 2. 推荐系统介绍：深入介绍推荐系统，包含详细的推荐系统分类，并对依赖推荐技术的两个系统（MovieLens 和 Amazon.com）进行巡览。在最后一课中有一个初步评估，以确保学员理解推荐的核心概念。 3. 非个性化与基于刻板印象的推荐：学习非个性化及轻度个性化推荐的几种技术，包括使用有意义的摘要统计、计算产品关联推荐以及利用人口统计数据进行轻度个性化探索。此模块设有作业（在电子表格中尝试这些技术）和测验以测试理解。 4. 基于内容的过滤 – 第一部分：介绍个性化的内容过滤技术，分为两周学习和实践基于内容的过滤的基本技巧，并探索推荐系统中使用的多种高级接口和计算技术。 5. 基于内容的过滤 – 第二部分：对基于内容的过滤的评估包括一个作业，计算三种类型的个人档案和预测，并有针对所学主题的测验。作业分为三个部分，包括书面作业、视频介绍和一个通过学员完成的工作自动评分的“测验”。 6. 课程总结：以一组对未来深入推荐系统（在该专业的后续课程中）有帮助的数学符号结束本课程。 本课程旨在为学员提供推荐系统的基础知识和实际操作技能，适合对推荐系统感兴趣的初学者。
Name:Preface
Description:This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.
Name:Introducing Recommender Systems
Description:This module introduces recommender systems in more depth. It includes a detailed taxonomy of the types of recommender systems, and also includes tours of two systems heavily dependent on recommender technology: MovieLens and Amazon.com. There is an introductory assessment in the final lesson to ensure that you understand the core concepts behind recommendations before we start learning how to compute them.
Name:Non-Personalized and Stereotype-Based Recommenders
Description:In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension.
Name:Content-Based Filtering -- Part I
Description:The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems.
Name:Content-Based Filtering -- Part II
Description:The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded.
Name:Course Wrap-up
Description:We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization).
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.
