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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.

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51 - 66 sur 66 Avis pour Nearest Neighbor Collaborative Filtering

par Daniel P

8 déc. 2017

Rather non-technical, interesting general information, plus voluntary programming assignment which I personally found little bit "bulky". More effort I spent to get familiar with the library than to actually use the collaborative filtering algorithms.

par Dan T

23 nov. 2017

I liked the course, assignment two for item item was so much harder than the user user piece. I really spent all my time fighting excel, rather that working on the problem. it would have been easier to program it in lenskit!

par Gui M T

1 avr. 2019

Much better than the first course, covers more interesting algorithms in more depth. The assignments can be clearer instructions. I also wish the lectures cover actual mathematical examples to work us through the algorithms

par Dhananjay G

2 févr. 2020

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.

par Edgar M

25 oct. 2016

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.

par Matheus H d C Z

17 févr. 2020

The last week assignments were really poor explained. There were no examples or clearly what to do.

par Dino A

24 oct. 2016

I think this is very useful for introductory, but it lacks some references for who wants go deeper.

par maria j S

3 déc. 2019

Overall good, except for assignment 2 which was poorly explained on one of the parts

par Siddhartha S B

15 mai 2020

Excel coursework is good, evaluations are not that good.

par H M

21 juil. 2021

課題が求めていることが課題の説明文だけからではわかりにくく、無駄な時間を過ごさなければならないときがある。

par Jean-Paul R

19 juil. 2021

Very good course, but the quiz on Week 4 is unclear

par Elias A H

28 août 2017

The content of the course is actually great, the assignments are a bit challenging which was very interesting. I've learned a lot.

Nevertheless, I didn't enjoy the course much because the support to the course which is inexistent, forum's are almost empty. If you answer a question, you have maybe 1% chance to get an answer from someone, if you open a discussion, it ends up being a monologue...

par VenusW

27 janv. 2021

Very great course content.

However, no example show the computation work.

Assignment instruction is too vague, has no updates for years, have to look through explanation on Discussion Forum, wasted a lot of time and still no clue...

par Yiwen X

23 juil. 2020

Good content, but the slides can be more concise

par PRATIK K C

1 juin 2020

Waiting to see assignments in Python.

par Chunyang S

24 févr. 2017

The content is too basic, and both lectures are too boring.