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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8,313 已注册
教学大纲 - 您将从这门课程中学到什么
周
1周
2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.
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
周
3完成时间为 4 小时
Matrix Factorization (Part 2)
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
周
4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.
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
周
5完成时间为 23 分钟
Advanced Machine Learning
3 个视频 （总计 23 分钟）
3 个视频
Learning to Rank: Interview with Xavier Amatriain21
Personalized Ranking (with Daniel Kluver)11分钟
周
6完成时间为 6 小时
Advanced Topics
7 个视频 （总计 133 分钟）, 1 个阅读材料, 3 个测验
7 个视频
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)22分钟
Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)18分钟
Industry Practical Issues: Inteview with Anmol Bhasin26分钟
Recommending Music - Interview with Paul Lamere13分钟
Specialization Wrap Up21分钟
Programming Hybrids & Learning to Rank9分钟
1 个阅读材料
Programming Hybrids and Machine Learning Description10分钟
2 个练习
Hybrid and Advanced Techniques Quiz12分钟
Honors Hybrid Assignment Evaluation Quiz10分钟
4.2
18 个审阅50%
通过此课程获得实实在在的工作福利
热门审阅
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
Awesome course especially for those doing Ph.D in recommender systems
讲师
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
关于 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.
关于 推荐系统 专项课程
This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.
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