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學生對 明尼苏达大学 提供的 Nearest Neighbor Collaborative Filtering 的評價和反饋

4.3
294 個評分
66 條評論

課程概述

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
2019年12月11日

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
2019年3月30日

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 個評論(共 66 個)

創建者 karthik n

2018年8月10日

(+) 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.

創建者 Yonaton H

2019年9月22日

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.

創建者 Alex B

2019年8月25日

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.

創建者 Jack B

2017年10月24日

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.

創建者 Srikanth K S

2017年1月5日

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

創建者 Domenico P

2017年11月20日

Some exercises have wrong directions !!!

創建者 LU W

2018年8月31日

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.

創建者 naveen r

2018年2月4日

Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!

創建者 Laurent B

2018年2月5日

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

創建者 Ashish P

2020年3月31日

Basic concepts were introduced really well and the assignments were helpful in applying the same, but a major portion of the course was covered with interviews with experts. The interviews were informative but didn't help much in getting hands-on experience about the stuff they were talking about. It would be a whole lot better if there were some mathematical exercises involved related to the advanced concepts introduced by the experts.

創建者 Daniel M

2019年6月23日

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.

創建者 Akash S C

2019年7月21日

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.

創建者 Danill B

2018年7月31日

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

創建者 Anyu S

2018年4月29日

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.

創建者 Daniil

2019年6月19日

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

創建者 Arun R

2019年12月1日

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

創建者 Ankit A

2018年6月21日

Week 4 assignments can do with a bit more clarity.

創建者 Alberto G

2018年3月26日

Assignments are not explained so well on this one

創建者 Zhenyu Z

2018年2月21日

the hands-on quiz is not well prepared.

創建者 Kemal C K

2017年3月7日

Lessons need more examples.

創建者 Gregory R

2017年4月19日

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).

創建者 Jose R

2018年5月27日

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

創建者 Konstantinos P

2017年4月10日

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

創建者 Deleted A

2020年4月2日

Extremely subpar.

創建者 nic w

2017年9月2日

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!