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.
提供方
推荐系统 專項課程明尼苏达大学課程信息
3,385 次近期查看
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 5 門）
完成時間大約為15 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 5 門）
完成時間大約為15 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
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.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 13 分鐘
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.
完成時間為 13 分鐘
1 個視頻 （總計 3 分鐘）, 1 個閱讀材料
完成時間為 1 小時
User-User Collaborative Filtering Recommenders Part 1
完成時間為 1 小時
5 個視頻 （總計 85 分鐘）
完成時間為 5 小時
User-User Collaborative Filtering Recommenders Part 2
完成時間為 5 小時
2 個視頻 （總計 13 分鐘）, 2 個閱讀材料, 3 個測驗
完成時間為 1 小時
Item-Item Collaborative Filtering Recommenders Part 1
完成時間為 1 小時
6 個視頻 （總計 70 分鐘）
完成時間為 6 小時
Item-Item Collaborative Filtering Recommenders Part 2
完成時間為 6 小時
2 個視頻 （總計 10 分鐘）, 2 個閱讀材料, 5 個測驗
完成時間為 2 小時
Advanced Collaborative Filtering Topics
完成時間為 2 小時
5 個視頻 （總計 73 分鐘）
審閱
來自NEAREST NEIGHBOR COLLABORATIVE FILTERING的熱門評論
由 NS 提供2019年12月11日
i found this course very helpful and informative. it explains the theory while providing real-world examples on recommender systems. the assignment helps in clearing up any confusion with the material
由 SS 提供2019年3月30日
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.
由 DG 提供2020年2月1日
I found this course very informative and clears lot of concept in Item based and used based collaborative filtering. Spreadsheet assignment helped me to clearly understand the algorithms.
由 EM 提供2016年10月24日
Very good content ! Very interesting interviews with expert in the field that shows real examples. However the exercise needs a bit more work to be very useful.
關於 推荐系统 專項課程
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.
常見問題
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
有助学金吗？
還有其他問題嗎？請訪問 學生幫助中心。
