关于此课程： In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
制作方： 明尼苏达大学
教学方： Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering教学方： Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| 基本信息 | 课程 2（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
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
1 个视频, 1 个阅读材料
- Reading: Course Structure Outline
User-User Collaborative Filtering Recommenders Part 1
5 个视频
- 视频: Configuring User-User Collaborative Filtering
- 视频: Influence Limiting and Attack Resistance; Interview with Paul Resnick
- 视频: Trust-Based Recommendation; Interview with Jen Golbeck
- 视频: Impact of Bad Ratings; Interview with Dan Cosley
第 2 周
User-User Collaborative Filtering Recommenders Part 2
2 个视频, 2 个阅读材料
- Reading: Assignment Instructions: User-User CF
- Reading: Introducing User-User CF Programming Assignment
- 视频: Programming Assignment - Programming User-User Collaborative Filtering
已评分: User-User CF Answer Sheet
已评分: User-User Collaborative Filtering Quiz
已评分: User-User CF Programming Assignment
第 3 周
Item-Item Collaborative Filtering Recommenders Part 1
6 个视频
- 视频: Item-Item Algorithm
- 视频: Item-Item on Unary Data
- 视频: Item-Item Hybrids and Extensions
- 视频: Strengths and Weaknesses of Item-Item Collaborative Filtering
- 视频: Interview with Brad Miller
第 4 周
Item-Item Collaborative Filtering Recommenders Part 2
2 个视频, 2 个阅读材料
- Reading: Item-Based CF Assignment Instructions
- Reading: Introducing Item-Item CF Programming Assignment
- 视频: Programming Assignment - Programming Item-Item Collaborative Filtering
已评分: Item Based Assignment Part l
已评分: Item Based Assignment Part II
已评分: Item Based Assignment Part III
已评分: Item Based Assignment Part IV
已评分: Item-Item CF Programming Assignment
Advanced Collaborative Filtering Topics
5 个视频
- 视频: Recommending for Groups: Interview with Anthony Jameson
- 视频: Threat Models
- 视频: Explanations
- 视频: Explanations, Part II: Interview with Nava Tintarev
已评分: Item-Based and Advanced Collaborative Filtering Topics Quiz
常见问题解答
运作方式
Coursework
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制作方
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.
价格
| 购买课程 | |
|---|---|
| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
评分和审阅
It would be better to provide other programming language such as python in honour assignment. And in the assignment should more emphasis on the algorithm not rely on too much others such as Lenskit.
(+) The course material is good with real world examples and interviews with different people.
(+) Interesting material
(-) The assignments had mistakes.
(-) There is no example provided for practice before jumping into assignments.
very nice
Diverse content that helps in understanding the basic concepts of collaborative filtering. Interviews with people specializing in different nuances of collaborative filteering were very useful.
Some thoughts on what could be improved
Pace of narration. It can be faster
More exercises are needed to get more familiar with the concepts. Each lecture should have a exercise (not just a quiz)