Chevron Left
Retour à Nearest Neighbor Collaborative Filtering

Avis et commentaires pour d'étudiants pour Nearest Neighbor Collaborative Filtering par Université du Minnesota

4.3
étoiles
294 évaluations
66 avis

À propos du cours

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....

Meilleurs avis

NS
11 déc. 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
30 mars 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.

Filtrer par :

26 - 50 sur 66 Avis pour Nearest Neighbor Collaborative Filtering

par Ayoub B

23 sept. 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.

par Keshaw S

13 févr. 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.

par Nesreen S

12 déc. 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

par Sorratat S

31 mars 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.

par Hossein E

13 déc. 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 !

par Ashwin R

4 août 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!

par Christian J

17 juil. 2017

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

par Dan S R

15 juin 2017

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

I love it!

par Pawel S

8 janv. 2017

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

par Sanjay K

16 janv. 2018

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

par Seema P

14 févr. 2017

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

par Apurva D

3 août 2017

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

par Light0617

20 juil. 2017

a great class, I learned some insight in these algorithms

par Hagay L

8 juil. 2019

Great learning experience about collaborative filtering!

par Ben C

17 nov. 2017

Exercises take time but really helpful.

par srikalyan

13 juin 2017

Very good assignments, honors track.

par Xin X

23 oct. 2017

in-depth and well-made to follow

par Xinzhi Z

23 juil. 2019

Nice course!

par Sushmita B

9 juin 2020

excellent

par Twinkle

30 avr. 2018

very nice

par Andrew W

20 janv. 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.

par Yury Z

22 mars 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.

par Jan Z

10 nov. 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.

par Siwei Y

27 nov. 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 .

par Ankur S

16 oct. 2018

Diverse content that helps in understanding the basic concepts of collaborative filtering. Interviews with people specializing in different nuances of collaborative filteering were very useful.

Some thoughts on what could be improved

Pace of narration. It can be faster

More exercises are needed to get more familiar with the concepts. Each lecture should have a exercise (not just a quiz)