About this course: 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.
Created by: University of Minnesota
Taught by: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
Taught by: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
| Basic Info | Course 4 of 5 in the Recommender Systems Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Preface
1 video
- Video: Matrix Factorization and Advanced Techniques
WEEK 2
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 videos, 1 reading
- Video: Introduction to Matrix Factorization and Dimensionality Reduction
- Video: Singular Value Decomposition
- Video: Gradient Descent Techniques
- 阅读: On Folding-In with Gradient Descent
- Video: Deriving FunkSVD
- Video: Probabilistic Matrix Factorization
WEEK 3
Matrix Factorization (Part 2)
2 videos, 2 readings
- Video: Assignment Introduction
- 阅读: Assignment Instructions
- 阅读: Intro - Programming Matrix Factorization
- Video: Programming Matrix Factorization
Graded: Matrix Factorization Assignment Part l
Graded: Matrix Factorization Assignment Part ll
Graded: Matrix Factorization Assignment Part lll
Graded: Matrix Factorization Quiz
Graded: Programming SVD
Graded: SVD Programming Eval Quiz
WEEK 4
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 videos
- Video: Hybrid Recommenders
- Video: Hybrids with Robin Burke
- Video: Hybridization through Matrix Factorization
- Video: Matrix Factorization Hybrids with George Karypis
- Video: Interview with Arindam Banerjee
- Video: Interview with Yehuda Koren
WEEK 5
Advanced Machine Learning
3 videos
- Video: Learning Recommenders
- Video: Learning to Rank: Interview with Xavier Amatriain
- Video: Personalized Ranking (with Daniel Kluver)
WEEK 6
Advanced Topics
7 videos, 1 reading
- Video: Context-Aware Recommendation I : Interview with Francesco Ricci
- Video: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)
- Video: Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)
- Video: Industry Practical Issues: Inteview with Anmol Bhasin
- Video: Recommending Music - Interview with Paul Lamere
- Video: Specialization Wrap Up
- 阅读: Programming Hybrids and Machine Learning Description
- Video: Programming Hybrids & Learning to Rank
Graded: Hybrid and Advanced Techniques Quiz
Graded: Programming Hybrids and Learning-to-Rank
Graded: Honors Hybrid Assignment Evaluation Quiz
FAQs
How It Works
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
Creators
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.
Pricing
| 购买课程 | 旁听 | |
|---|---|---|
| 访问课程材料 | Available | Available |
| 访问评分的材料 | Available | Not available |
| 收到最终成绩 | Available | Not available |
| 获得可共享的课程证书 | Available | Not available |
Ratings and Reviews
Interview with Francesco Ricci
is very knowledgeable about context aware Recommender System.
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
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EC
This is THE BEST course in Recommendation Systems. Probably getting a bit outdated as the field is moving rapidly into deep learning and other techniques, but the problems faced by any recommendation system developer would be the same - ratings measurement, sparse matrices, ranking metrics... And THAT is probably the best contribution of the whole training: not only talking about the methods but pin-pointing the main REAL problems to solve.
REALLY GOOD!!!!!!!