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学生对 明尼苏达大学 提供的 Nearest Neighbor Collaborative Filtering 的评价和反馈

4.3
296 个评分
67 条评论

课程概述

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....

热门审阅

NS

Dec 11, 2019

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

SS

Mar 30, 2019

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.

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26 - Nearest Neighbor Collaborative Filtering 的 50 个评论(共 67 个)

创建者 Ayoub B

Sep 23, 2020

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. Also, the Honors track assignments are very good, although I like using Java but would love to use Python instead.

创建者 Keshaw S

Feb 13, 2018

All in all, it is a comprehensive introduction to collaborative filtering. It allows the reader which paradigms and what tools to use in specific situations. I still have some complains with the excel assignments though.

创建者 Nesreen S

Dec 12, 2019

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

创建者 Sorratat S

Mar 31, 2019

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.

创建者 Hossein E

Dec 13, 2017

everything best. But technical support in Forum and when a student needs help when he is learning in Vienna alone is the worst

thanks very much !

创建者 Ashwin R

Aug 4, 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!

创建者 Christian J

Jul 17, 2017

Very good course, there is a glaring error in Week 4s assignment. But if you check the forums it can be easily solved

创建者 Dan R

Jun 15, 2017

Very satisfied to do this, the videos are too long, very good quality and a lot of practical information.

I love it!

创建者 Pawel S

Jan 8, 2017

I love it. Would be cool to be able download all materials in one big .zip file (e.g for searching using grep) ;-)

创建者 Sanjay K

Jan 16, 2018

Provides a good overview of item based and user based collaborative filtering approaches.

创建者 Seema P

Feb 14, 2017

Awesome Professors!Great Material.Very thankful to Coursera for providing this course.

创建者 Apurva D

Aug 3, 2017

Loved it...many thanks Prof. Joe for bringing this content to Coursera

创建者 Light0617

Jul 20, 2017

a great class, I learned some insight in these algorithms

创建者 Hagay L

Jul 8, 2019

Great learning experience about collaborative filtering!

创建者 Ben C

Nov 17, 2017

Exercises take time but really helpful.

创建者 srikalyan

Jun 13, 2017

Very good assignments, honors track.

创建者 Xin X

Oct 23, 2017

in-depth and well-made to follow

创建者 Blancher S

Apr 8, 2022

old, but very clear

创建者 Xinzhi Z

Jul 23, 2019

Nice course!

创建者 Sushmita B

Jun 9, 2020

excellent

创建者 Twinkle

Apr 30, 2018

very nice

创建者 Andrew W

Jan 20, 2018

Thank you for this course -- it opened my eyes to the universal applicability of recommender systems in tech applications.

My feedback is that you could do more to tie the *implementation* to the theory and real-life applications you discuss. You have many great lectures talking about how these systems were implemented, qualitative differences, subtle differences, and interviewing people to give us perspectives. But then the videos on implementation (including working through the equations) are pretty sparse and short. I felt like I'm "on my own" to figure out how to go implement these in real life. The problem sets cover one test case, and that's it. I think you could update the lectures to focus more on different algorithms / equations in different scenarios, rather than just talking qualitatively about them.

Regardless thank you! I deeply appreciate this course and what you've done. I plan to help my Consulting clients directly based on what I learned from you.

创建者 Yury Z

Mar 22, 2018

The topics I am interested in covered by people who definitely has related expertise. But overall quality of the teaching materials expected to be higher. Forum is also a little bit deserted, although contains some critical hints to pass the assignments (such a hints worth to be included in the assignment descriptions itself). I want to support the course, and it is pity to give it only 4 of 5 stars, but I really expect more quality from the course I paid for.

创建者 Jan Z

Nov 10, 2016

Excellent course providing not only the knowledge of algorithms but also useful insights into developing and maintaining recommender systems. Only thing that could use some work is the assignments. Spreadsheet assignment in week 4 is poorly designed (as evidenced by many forum threads with people not knowing what is it that the authors actually want). Other than that, that was an extremely helpful course.

创建者 Siwei Y

Nov 27, 2016

Overall , it is a very interesting course.

But I would like to say , that there are too many interviews. I think that it is a little bit difficult for some non-native speaker to understand the main and important things from the interview, because some interviewers talked in a very loose way. So I would suggest our teacher , to summarize the main points of those interview in a better way .