Chevron Left
返回到 Introduction to Recommender Systems: Non-Personalized and Content-Based

学生对 明尼苏达大学 提供的 Introduction to Recommender Systems: Non-Personalized and Content-Based 的评价和反馈

4.5
539 个评分
111 条评论

课程概述

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems....

热门审阅

BS

Feb 13, 2019

One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.

DP

Dec 08, 2017

Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).

筛选依据:

101 - Introduction to Recommender Systems: Non-Personalized and Content-Based 的 107 个评论(共 107 个)

创建者 MinhyePark

Feb 27, 2020

수학개념이 부족해서 조금 추상적으로 이해하게 되었습니다.

创建者 Oleg P

May 24, 2020

There is no math in this course and it does not use Python. Therefore this course does a terrible job of preparing you for interview questions on Recommender systems. Personally I thought this course was a waste of my time and money. However the final excel exercise actually had some useful information, but it was only a 10 minute exercise after many hours of useless lectures. I could have done the same exercise for free.

创建者 Alex B

Aug 26, 2019

This course mostly works. Contains a lot of wasted video time where no information is communicated. Uses simplistic tools that don't scale to data applications or otherwise dated tools not really used by data scientists or machine learning engineers making exercises either simplistic or a waste of time. Better than other courses in the series in that the assignments are legible.

创建者 Timea K

Jul 02, 2017

You should talk about music recommender systems as well! It was just OK, but boring some times... You were talking about lots of evident things by Amazon, making the course question. if it is seriously a university content.

创建者 Neha G

Nov 20, 2019

would give negative rating if it was possible, course appears non-cohesive and dispersed without any clear terminology being used in the videos. Assignments are not clear either.

创建者 Francisco R

Jul 07, 2020

Info desactualizada y no tiene la opción de usar python

创建者 andrew

Dec 12, 2016

the video is too long!