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第 2 门课程(共 5 门)
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完成时间大约为13 小时
英语(English)
字幕:英语(English)
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 2 门课程(共 5 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为13 小时
英语(English)
字幕:英语(English)

提供方

明尼苏达大学 徽标

明尼苏达大学

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 13 分钟

Preface

完成时间为 13 分钟
1 个视频 (总计 3 分钟), 1 个阅读材料
1 个视频
1 个阅读材料
Course Structure Outline10分钟
完成时间为 1 小时

User-User Collaborative Filtering Recommenders Part 1

完成时间为 1 小时
5 个视频 (总计 85 分钟)
5 个视频
Configuring User-User Collaborative Filtering9分钟
Influence Limiting and Attack Resistance; Interview with Paul Resnick21分钟
Trust-Based Recommendation; Interview with Jen Golbeck15分钟
Impact of Bad Ratings; Interview with Dan Cosley13分钟
2

2

完成时间为 5 小时

User-User Collaborative Filtering Recommenders Part 2

完成时间为 5 小时
2 个视频 (总计 13 分钟), 2 个阅读材料, 3 个测验
2 个视频
Programming Assignment - Programming User-User Collaborative Filtering4分钟
2 个阅读材料
Assignment Instructions: User-User CF10分钟
Introducing User-User CF Programming Assignment10分钟
2 个练习
User-User CF Answer Sheet48分钟
User-User Collaborative Filtering Quiz20分钟
3

3

完成时间为 1 小时

Item-Item Collaborative Filtering Recommenders Part 1

完成时间为 1 小时
6 个视频 (总计 70 分钟)
6 个视频
Item-Item Algorithm16分钟
Item-Item on Unary Data6分钟
Item-Item Hybrids and Extensions4分钟
Strengths and Weaknesses of Item-Item Collaborative Filtering9分钟
Interview with Brad Miller16分钟
4

4

完成时间为 4 小时

Item-Item Collaborative Filtering Recommenders Part 2

完成时间为 4 小时
2 个视频 (总计 10 分钟), 2 个阅读材料, 5 个测验
2 个视频
Programming Assignment - Programming Item-Item Collaborative Filtering4分钟
2 个阅读材料
Item-Based CF Assignment Instructions10分钟
Introducing Item-Item CF Programming Assignment10分钟
4 个练习
Item Based Assignment Part l10分钟
Item Based Assignment Part II10分钟
Item Based Assignment Part III10分钟
Item Based Assignment Part IV10分钟
完成时间为 2 小时

Advanced Collaborative Filtering Topics

完成时间为 2 小时
5 个视频 (总计 73 分钟)
5 个视频
Recommending for Groups: Interview with Anthony Jameson14分钟
Threat Models11分钟
Explanations16分钟
Explanations, Part II: Interview with Nava Tintarev17分钟
1 个练习
Item-Based and Advanced Collaborative Filtering Topics Quiz20分钟

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关于 推荐系统 专项课程

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. 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....
推荐系统

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