In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
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第 4 门课程（共 5 门）
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完成时间大约为8 小时
建议：11 hours/week
英语（English）
字幕：英语（English）
提供方
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 1 小时
Matrix Factorization (Part 1)
This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.
完成时间为 1 小时
5 个视频 （总计 70 分钟）, 1 个阅读材料
5 个视频
Singular Value Decomposition17分钟
Gradient Descent Techniques17分钟
Deriving FunkSVD11分钟
Probabilistic Matrix Factorization10分钟
1 个阅读材料
On Folding-In with Gradient Descent10分钟
完成时间为 4 小时
Matrix Factorization (Part 2)
完成时间为 4 小时
2 个视频 （总计 15 分钟）, 2 个阅读材料, 6 个测验
2 个阅读材料
Assignment Instructions10分钟
Intro - Programming Matrix Factorization10分钟
5 个练习
Matrix Factorization Assignment Part l10分钟
Matrix Factorization Assignment Part ll10分钟
Matrix Factorization Assignment Part lll10分钟
Matrix Factorization Quiz8分钟
SVD Programming Eval Quiz6分钟
完成时间为 2 小时
Hybrid Recommenders
This is a three-part, two-week module on hybrid and machine learning recommendaton algorithms and advanced recommender techniques. It includes a quiz (due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.
完成时间为 2 小时
6 个视频 （总计 96 分钟）
6 个视频
Hybrids with Robin Burke16分钟
Hybridization through Matrix Factorization15分钟
Matrix Factorization Hybrids with George Karypis17分钟
Interview with Arindam Banerjee15分钟
Interview with Yehuda Koren22分钟
审阅
4.3
来自MATRIX FACTORIZATION AND ADVANCED TECHNIQUES的热门评论
由 AG 提供Jun 9, 2018
Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.
由 LL 提供Jul 18, 2017
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
由 AA 提供Aug 13, 2017
Interview with Francesco Ricci\n\nis very knowledgeable about context aware Recommender System.
由 SL 提供Sep 11, 2019
It will be great, if we can do honor's track with Python or R
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.
This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics.
The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
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