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.
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推荐系统 专项课程明尼苏达大学课程信息
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第 1 门课程（共 5 门）
中级
完成时间大约为23 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
- Summary Statistics
- Term Frequency Inverse Document Frequency (TF-IDF)
- Microsoft Excel
- Recommender Systems
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 5 门）
中级
完成时间大约为23 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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明尼苏达大学
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
授课大纲 - 您将从这门课程中学到什么
完成时间为 1 小时
Preface
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.
完成时间为 1 小时
2 个视频 （总计 41 分钟）, 1 个阅读材料
完成时间为 4 小时
Introducing Recommender Systems
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.
完成时间为 4 小时
9 个视频 （总计 147 分钟）, 2 个阅读材料, 2 个测验
完成时间为 10 小时
Non-Personalized and Stereotype-Based Recommenders
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.
完成时间为 10 小时
7 个视频 （总计 111 分钟）, 5 个阅读材料, 9 个测验
完成时间为 3 小时
Content-Based Filtering -- Part I
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.
完成时间为 3 小时
8 个视频 （总计 156 分钟）
完成时间为 6 小时
Content-Based Filtering -- Part II
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.
完成时间为 6 小时
2 个视频 （总计 26 分钟）, 3 个阅读材料, 3 个测验
完成时间为 1 小时
Course Wrap-up
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).
完成时间为 1 小时
2 个视频 （总计 45 分钟）, 1 个阅读材料
审阅
- 5 stars60.65%
- 4 stars29.43%
- 3 stars6.34%
- 2 stars1.78%
- 1 star1.78%
来自INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED的热门评论
由 MQ 提供Mar 7, 2017
Overall, the class is perfect. But if you could supply a sample of honour class when we have finished honour codes, it would be perfect.
由 KR 提供Oct 8, 2017
Well-designed assignments and instructive programming exercises in the honors track.
由 PD 提供Jun 24, 2017
Great, thorough introduction with tracks for both Java programmers and non-programmers.
由 AR 提供Aug 29, 2020
Great intro to recommendation systems, the course is well structured and engaging to all students of different backgrounds.
关于 推荐系统 专项课程
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
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How does this course relate to the prior versions of "Introduction to Recommender Systems"?
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