关于此课程： 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.
制作方： University of Minnesota
教学方： Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University教学方： Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
| 基本信息 | 课程 4（共 5 门，Recommender Systems Specialization ） |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
Preface
1 个视频
第 2 周
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, 1 个阅读材料
- 视频: Singular Value Decomposition
- 视频: Gradient Descent Techniques
- Reading: On Folding-In with Gradient Descent
- 视频: Deriving FunkSVD
- 视频: Probabilistic Matrix Factorization
第 3 周
Matrix Factorization (Part 2)
2 个视频, 2 个阅读材料
- Reading: Assignment Instructions
- Reading: Intro - Programming Matrix Factorization
- 视频: Programming Matrix Factorization
已评分: Matrix Factorization Assignment Part l
已评分: Matrix Factorization Assignment Part ll
已评分: Matrix Factorization Assignment Part lll
已评分: Matrix Factorization Quiz
已评分: Programming SVD
已评分: SVD Programming Eval Quiz
第 4 周
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
- 视频: Hybrids with Robin Burke
- 视频: Hybridization through Matrix Factorization
- 视频: Matrix Factorization Hybrids with George Karypis
- 视频: Interview with Arindam Banerjee
- 视频: Interview with Yehuda Koren
第 5 周
Advanced Machine Learning
3 个视频
- 视频: Learning to Rank: Interview with Xavier Amatriain
- 视频: Personalized Ranking (with Daniel Kluver)
第 6 周
Advanced Topics
7 个视频, 1 个阅读材料
- 视频: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)
- 视频: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)
- 视频: Industry Practical Issues: Inteview with Anmol Bhasin
- 视频: Recommending Music - Interview with Paul Lamere
- 视频: Specialization Wrap Up
- Reading: Programming Hybrids and Machine Learning Description
- 视频: Programming Hybrids & Learning to Rank
已评分: Hybrid and Advanced Techniques Quiz
已评分: Programming Hybrids and Learning-to-Rank
已评分: Honors Hybrid Assignment Evaluation Quiz
常见问题解答
运作方式
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
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Certificates
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制作方
University of Minnesota
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.
价格
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评分和审阅
Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.
Based on my experience with the previous courses in this specialization, I was very positively surprised by the amount and depth of material provided in this course. It covers almost everything that is there to be known. The comprehensive interviews are a big plus point. Also, this course provides guidelines as to how to develop, employ and evaluate a recommender system in real life. I would definitely recommend anyone interested in the field to take this course.
The Hybrid recommenders i.e. Week 4 needs more explanation especially what is Tensor factorization etc, Week 4 was difficult to grasp. Week 5 and Week 6 was informative especially the LinkedIn video and Learning to Rank: Interview with Xavier Amatriain in which a problem was discussed of having that popularity vs Ranking issue.
Thanks it was quite a lot new things to learn ! My Rating 4.5
It would be better if explaining how to build latent features