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Nearest Neighbor Collaborative Filtering(으)로 돌아가기

Nearest Neighbor Collaborative Filtering, 미네소타 대학교

4.3
(191개의 평가)

About this Course

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....

최상위 리뷰

대학: SS

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.

대학: NR

Feb 04, 2018

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!

필터링 기준:

44개의 리뷰

대학: Gui Ming Tang

Apr 01, 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

대학: Sorratat Sirirattanajakarin

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.

대학: Ankur Shrivastav

Oct 16, 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)

대학: LU WEI

Sep 01, 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.

대학: 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.

대학: Daniil Barysevich

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

대학: Ankit Agarwal

Jun 21, 2018

Week 4 assignments can do with a bit more clarity.

대학: Jose Robalo

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

대학: Twinkle

Apr 30, 2018

very nice

대학: Anyu Slofstra

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.