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.
提供：
このコースについて
7,168 最近の表示
このCourseを受講している学習者は
- Data Scientists
- Machine Learning Engineers
- Data Engineers
- Business Analysts
- Analysts (General)
シラバス - 本コースの学習内容
週
113分で修了
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件のビデオ （合計3分）, 1 reading
1件のビデオ
1件の学習用教材
Course Structure Outline10 分
1時間で修了
User-User Collaborative Filtering Recommenders Part 1
5件のビデオ （合計85分）
5件のビデオ
Configuring User-User Collaborative Filtering9 分
Influence Limiting and Attack Resistance; Interview with Paul Resnick21 分
Trust-Based Recommendation; Interview with Jen Golbeck15 分
Impact of Bad Ratings; Interview with Dan Cosley13 分
週
25時間で修了
User-User Collaborative Filtering Recommenders Part 2
2件のビデオ （合計13分）, 2 readings, 3 quizzes
2件のビデオ
Programming Assignment - Programming User-User Collaborative Filtering4 分
2件の学習用教材
Assignment Instructions: User-User CF10 分
Introducing User-User CF Programming Assignment10 分
2の練習問題
User-User CF Answer Sheet48 分
User-User Collaborative Filtering Quiz20 分
週
31時間で修了
Item-Item Collaborative Filtering Recommenders Part 1
6件のビデオ （合計70分）
6件のビデオ
Item-Item Algorithm16 分
Item-Item on Unary Data6 分
Item-Item Hybrids and Extensions4 分
Strengths and Weaknesses of Item-Item Collaborative Filtering9 分
Interview with Brad Miller16 分
週
44時間で修了
Item-Item Collaborative Filtering Recommenders Part 2
2件のビデオ （合計10分）, 2 readings, 5 quizzes
2件のビデオ
Programming Assignment - Programming Item-Item Collaborative Filtering4 分
2件の学習用教材
Item-Based CF Assignment Instructions10 分
Introducing Item-Item CF Programming Assignment10 分
4の練習問題
Item Based Assignment Part l10 分
Item Based Assignment Part II10 分
Item Based Assignment Part III10 分
Item Based Assignment Part IV10 分
2時間で修了
Advanced Collaborative Filtering Topics
5件のビデオ （合計73分）, 1 quiz
5件のビデオ
Recommending for Groups: Interview with Anthony Jameson14 分
Threat Models11 分
Explanations16 分
Explanations, Part II: Interview with Nava Tintarev17 分
1の練習問題
Item-Based and Advanced Collaborative Filtering Topics Quiz20 分
4.3
50件のレビューNearest Neighbor Collaborative Filtering からの人気レビュー
Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.
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!
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
ミネソタ大学（University of Minnesota）について
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.
レコメンダシステム専門講座について
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics.
The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
よくある質問
いつ講座や課題にアクセスできるようになりますか？
修了証に登録すると、すべてのビデオ、テスト、およびプログラミング課題（該当する場合）にアクセスできます。ピアレビュー課題は、セッションが開始してからのみ、提出およびレビューできます。購入せずにコースを検討することを選択する場合、特定の課題にアクセスすることはできません。
この専門講座をサブスクライブすると何を行うことができるようになりますか？
コースに登録する際、専門講座のすべてのコースにアクセスできます。コースの完了時には修了証を取得できます。電子修了証が成果のページに追加され、そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。
返金ポリシーについて教えてください。
学資援助はありますか？
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。