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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

471 个评分
96 条评论


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....



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.


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).


26 - Introduction to Recommender Systems: Non-Personalized and Content-Based 的 50 个评论(共 92 个)

创建者 Ashwin R

Jun 26, 2017

An excellent in-depth introduction into the concepts around recommendation systems!

创建者 Xinzhi Z

Jul 18, 2019

Great course. I really appreciated the efforts spent by the course team.

创建者 shayue

Apr 11, 2019

Really Good! I think it will be helpful to me and take a job for me!

创建者 Light0617

Jul 19, 2017

great!! Let me better understand the research and practical fields!

创建者 Luis D F

Apr 17, 2017

Really good course to get started with recommendation systems!

创建者 Apurva D

Aug 03, 2017

Awesome content...loved the industry expert interviews....

创建者 Dan T

Oct 31, 2017

great overview of the breadth of material to get started

创建者 S A

Jun 30, 2017

Excellent course taught in simple language.

创建者 Biswa s

Mar 28, 2018

Good overview on the recommend-er system.

创建者 Shuang L

Nov 21, 2017

great professors and inspiring lectures!

创建者 王嘉奕

Nov 06, 2019

Excellent course which helps me a lot.

创建者 Su L

Aug 23, 2019

great course, learnt a lot, thanks!

创建者 Fernando C

Nov 08, 2016

pues esta bien chido el curso

创建者 Mai H S

Jan 20, 2019

good exercises & lectures

创建者 Julia E

Nov 08, 2017

Thank you very much!

创建者 sagar s

Oct 04, 2018

Awesome. Worth it!

创建者 Garvit G

Mar 22, 2018

awesome course.

创建者 jonghee

Oct 29, 2019

good lecture

创建者 Mustafa S

Feb 08, 2019

Great course

创建者 P S

Sep 26, 2019

Nice course

创建者 Muhammad Z H

Sep 17, 2019

Learnt alot

创建者 姚青桦

Oct 16, 2017

Pretty good

创建者 HN M

Aug 28, 2017


创建者 Aussie P

Jul 02, 2017

Well prepared course. In-depth lecture. Easy to follow even when listening only. The course lectures is very detailed, and that is one thing I really liked. The videos does feel a bit long, and maybe we can chop it to smaller sub-topics.

The interviews are very interesting and show a glimpse of broader universe of recommendation system. However, the concepts explained in the interview is a bit hard to follow, as there is no accompanying presentation materials and it jumps to detailed content with little context

The regular exercise feels very easy but helpful to make the concepts concrete. The Honors programming exercise looks interesting & challenging, but it seems too hard for someone with no programming background. I am also learning Python in parallel, so I decided to drop it to avoid learning 2 languages in parallel.

创建者 Ankur S

Sep 25, 2018

Very informative, very well organized. Especially like the questions like "Which domain would this technique most likely to apply".

Some areas of improvement to consider

The overall pace of the content delivery in various lectures could be increased. Tends to get very slow at times

More hands on exercises would be useful

Programming exercise in Python or Python based frameworks would bee useful