最近邻居协同过滤

Nearest Neighbor Collaborative Filtering

1395 次查看
明尼苏达大学
Coursera
  • 完成时间大约为 12 个小时
  • 混合难度
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

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.

课程大纲

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.

User-User Collaborative Filtering Recommenders Part 1

User-User Collaborative Filtering Recommenders Part 2

Item-Item Collaborative Filtering Recommenders Part 1

Item-Item Collaborative Filtering Recommenders Part 2

Advanced Collaborative Filtering Topics

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