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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
281 个评分
63 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....

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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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1 - Nearest Neighbor Collaborative Filtering 的 25 个评论(共 63 个)

创建者 karthik n

Aug 10, 2018

(+) The course material is good with real world examples and interviews with different people.

(+) Interesting material

(-) The assignments had mistakes.

(-) There is no example provided for practice before jumping into assignments.

创建者 Jack B

Oct 24, 2017

The course is less helpful than the others in the specialty. The lecture should include an example to help clarify the understanding necessary for Quiz Part II and Part IV. The instructors didn't respond to the many questions in Week 4 forum and I was unable to complete the course.

创建者 Srikanth K S

Jan 5, 2017

instructions for assignments are not clear! Lectures are good, but its practically impossible to get the certificate.

创建者 Domenico P

Nov 20, 2017

Some exercises have wrong directions !!!

创建者 LU W

Aug 31, 2018

It would be better to provide other programming language such as python in honour assignment. And in the assignment should more emphasis on the algorithm not rely on too much others such as Lenskit.

创建者 Laurent B

Feb 5, 2018

There is an error in the assignment week 4 : the spreadsheet normalize by user instead of by item

创建者 Daniel M

Jun 23, 2019

The course material is good, but the course itself is merely okay due to some problems with the assignments that have gone unaddressed for years. The Item-Item filtering assignment solution does not match the formula given in the lectures, and the honors assignments use an outdated version of the code (at one point recommending a package that has been deprecated). Really needs some attention to fix bugs and update the software.

创建者 Yonaton N H

Sep 22, 2019

There is good information in this course but there are so many problems in this course. There are major errors in the assignments and I was only about the get the right answers by reading the discussions on the message board. There are coding exercises but they expect you to write them in Java rather than a language used by data scientists such as Python or R. It is a good thing the made them optional.

创建者 Akash S C

Jul 21, 2019

good introduction to topics and algorithms but very little help provided for the assignment in clarifying doubts in forums and unclear explanations were given for assignments. also not providing option to use any other programming language like python or r to do programming assignment is a big miss. would still recommend this course to get started from basics about reco sys.

创建者 Daniil B

Jul 31, 2018

The course itself is interesting, but some of the programming assignments are horribly confusing, what makes you waste your time trying to decipher what the professor really meant. Spreadsheet assignment on Week 3 is the main reason I rate this course so low, and a lot of people on discussion forums agree with me on assignment quality

创建者 Anyu S

Apr 29, 2018

Making honours programming exercise in Java is a mistake. Pls consider Python in the future. Assignment for week 4 uses formula differs from the course: wasted many hours that don't benefit learning.

创建者 Daniil

Jun 19, 2019

The course is pretty good, but the spreadsheet assignments are brutal: they are confusing, too tedious and don't have enough information to debug.

创建者 Arun R

Dec 1, 2019

THe item based assignment, parts II and IV didn't give enough guidance. Otherwise a decent course.

创建者 Ankit A

Jun 21, 2018

Week 4 assignments can do with a bit more clarity.

创建者 Alberto G

Mar 26, 2018

Assignments are not explained so well on this one

创建者 Zhenyu Z

Feb 21, 2018

the hands-on quiz is not well prepared.

创建者 Kemal C K

Mar 7, 2017

Lessons need more examples.

创建者 Gregory R

Apr 19, 2017

The content of the course is extremely useful, however assignments need review as the exercises results have mistakes and they are not explained very well (missing step by step guidance).

创建者 Jose R

May 27, 2018

Not clear examples in my opinion, and there was same complain made from several user and I never saw a reply and nothing was changed

创建者 Konstantinos P

Apr 10, 2017

Unfortunately, the content of the course is poor. Too many interviews and some of them are pointless.

创建者 Alex B

Aug 25, 2019

This course is taught at a really low level. Exercises are in spreadsheets which are more or less useless for practicing scale data applications. Spreadsheets contain information that makes importation into numerical processing software such as Pandas in Python or dplyr in R needlessly difficult and assumes the user can't even apply the distance formula.

Videos contain useful information but require wading through a lot of garbage at a slow pace, not useful for practitioners.

Assignments are poorly worded and some terminology is used questionably or flexibly (see the word "normalization"). Some assignments are so poorly done that there is an ongoing debate on the forums as to whether the autograder is messed up or the assignment instructions are messed up.

The "honors" track programming assignments use some piece of software with questionable generalizability. If I ever see lens kit in my own data work environment I will come back an edit my review but I find it unlikely. Furthermore, Java is not commonly used for data science or machine learning purposes making these assignments inaccessible to many users. Personally, I write in Java but I didn't find it fulfilling to waste my time playing "fill in the blanks" or "guess the library function" which is overall uninstructive.

Quiz assignments show true indications of the poor level of instruction. Recitation of pieces of information buried in 30 minutes videos that can be condensed into 5 are some of the finest examples of bad teaching. Regurgitating information found in required readings shows no level of comprehension of course material and is a severe disservice to students.

I will hope for better general coverage of recommender systems in the future in another course. Ideally using something applicable like Python, Scala (Spark), or even R.

创建者 Deleted A

Apr 2, 2020

Extremely subpar.

创建者 Nicolau L W

Sep 2, 2017

Great course, nice theory and interesting exercise with the sheets and making actual Java programs to implement the algorithms. I would love to see some more in-depth probability theory, and considerations about when the algorithms deviate from the theory, or connections to other theories, but I suppose the course is more accessible and interesting like this. The interviews are probably my favorite part!

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.