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.
教學大綱 - 您將從這門課程中學到什麼
週
1Preface
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 個視頻 （總計 3 分鐘）, 1 個閱讀材料
1 個視頻
1 個閱讀材料
Course Structure Outline10分鐘
User-User Collaborative Filtering Recommenders Part 1
5 個視頻 （總計 85 分鐘）
5 個視頻
Configuring User-User Collaborative Filtering9分鐘
Influence Limiting and Attack Resistance; Interview with Paul Resnick21分鐘
Trust-Based Recommendation; Interview with Jen Golbeck15分鐘
Impact of Bad Ratings; Interview with Dan Cosley13分鐘
週
2User-User Collaborative Filtering Recommenders Part 2
2 個視頻 （總計 13 分鐘）, 2 個閱讀材料, 3 個測驗
2 個視頻
Programming Assignment - Programming User-User Collaborative Filtering4分鐘
2 個閱讀材料
Assignment Instructions: User-User CF10分鐘
Introducing User-User CF Programming Assignment10分鐘
2 個練習
User-User CF Answer Sheet48分鐘
User-User Collaborative Filtering Quiz20分鐘
週
3Item-Item Collaborative Filtering Recommenders Part 1
6 個視頻 （總計 70 分鐘）
6 個視頻
Item-Item Algorithm16分鐘
Item-Item on Unary Data6分鐘
Item-Item Hybrids and Extensions4分鐘
Strengths and Weaknesses of Item-Item Collaborative Filtering9分鐘
Interview with Brad Miller16分鐘
週
4Item-Item Collaborative Filtering Recommenders Part 2
2 個視頻 （總計 10 分鐘）, 2 個閱讀材料, 5 個測驗
2 個視頻
Programming Assignment - Programming Item-Item Collaborative Filtering4分鐘
2 個閱讀材料
Item-Based CF Assignment Instructions10分鐘
Introducing Item-Item CF Programming Assignment10分鐘
4 個練習
Item Based Assignment Part l10分鐘
Item Based Assignment Part II10分鐘
Item Based Assignment Part III10分鐘
Item Based Assignment Part IV10分鐘
Advanced Collaborative Filtering Topics
5 個視頻 （總計 73 分鐘）, 1 個測驗
5 個視頻
Recommending for Groups: Interview with Anthony Jameson14分鐘
Threat Models11分鐘
Explanations16分鐘
Explanations, Part II: Interview with Nava Tintarev17分鐘
1 個練習
Item-Based and Advanced Collaborative Filtering Topics Quiz20分鐘
4.3
42 個審閱熱門審閱
創建者 NR•Feb 4th 2018
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
創建者 AR•Aug 4th 2017
Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!
講師
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
關於 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.
關於 推荐系统 專項課程
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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