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Learner Reviews & Feedback for Introduction to Recommender Systems: Non-Personalized and Content-Based by University of Minnesota

4.5
stars
550 ratings
116 reviews

About the Course

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....

Top reviews

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).

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76 - 100 of 112 Reviews for Introduction to Recommender Systems: Non-Personalized and Content-Based

By Alejo P

Sep 13, 2019

The course is really well oriented, topics are broadly covered with good explanations and examples. One major drawback of this course is that the honors track is not implemented in Python, though I believe that possibly in future versions this will be adapted. In my case, the two options left are either I learn Java programming or I do not take the honors track.

By Jan Z

Oct 20, 2016

The course authors did a great job explaining concepts related to recommender systems. However, the programming assignments require Java usage, even though they could easily allow people to use different software, by just explaining the required algorithm and accepting a csv file with orderings/predictions. That was quite disappointing.

By Keshaw S

Feb 02, 2018

Some of the assignments are not particularly well created, in the sense that they seem to emphasize on recalling rather than learning, Also, most of the interview failed to hold my attention in general.

Overall, however, this is a very good course and gives a comprehensive overview of the prevalent techniques in the relevant fields.

By Hagay L

Jun 16, 2019

Overall a good course that teaches the basics for content based recommenders.

Would be great if the assignments were a bit more challenging, e.g.: work with large datasets (and not the tiny datasets used in the assignments)

Would also be good if we were provided papers of recent/notable research on the topic to read further.

By LI Z

Jan 01, 2019

Awesome lecture and demonstration.

Here are some suggestions, first I think this course may spend too much time on non-trivial parts and some parts can be neglected; second, the programming assignment lacks a lot of supplementary tutorial for people who are not familiar with Java and LensKit package.

By Abou-Haydar E

Nov 22, 2016

I love the course's content but discussions are of poor quality and the honors tracks assignments are a little messy. I ought having more explanation about the tool to use or maybe doing the programming assignments in another tool/language than Lenskit even it seems like a decent project.

By scott t

Aug 03, 2017

first time taking a course using Coursera...material was very interesting and well explained. I wish there was a way to speed up the audio track a little to shorten the lecture length. hard for the lecturer to engage with an audience that is not there, but both tried to do so.

By Dhananjay G

Dec 21, 2019

I found this course very useful for me to get in to basics and back ground of recommendations. Each topic is presented and discussed quite in detail . I also found the interviews with various expert in Recommendations very insightful. Thanks you Joe and Micheal.

By Swetha P S

Oct 25, 2017

Very informative course! I had a great learning experience working on the programming assignments required for honors. The only drawback is the style of communication (written and spoken) is elaborate and confuses many non-native English speakers including me.

By Abhisek G

Jun 05, 2017

There is a need to have this course in Python or some other statistical programming language. Simple reason is that a lot of budding data scientists are not coming from CS background and dont have necessary skillset in Java. Else the course is good.

By Rahul R

Jun 10, 2018

I think some of the interviews didn't really give me great insights. I know this is only an introduction, but I was expecting more fields than movies. I am overly critical though, all in all a very good way to understand recommendation systems.

By shailesh k p

Jun 22, 2018

I am very new to recommendation system and yet able to comprehend the lessons. The best thing is explaining the system with example. Walking through Amazon.com and explaining content based and collaborative filtering is easy to grasp.

By Lucia P

Jul 30, 2020

Interesting course, good overview, and presentation of the topic to those who are not familiar with RS.

Could have been 5 stars if the "developer" modules were available on Python. That's a big fail.

By Diana H

Jul 29, 2017

I think it could be fun if there were simple assignments which could be done in python. Java can be a bit heavy and a lot of the time goes with figuring out the framework. :)

By Nitish A

Apr 07, 2020

The course and its content was quite interesting and easy, so I will be taking the next course in this specialization of Recommender System Specialization

By Lucas B A d A

Apr 03, 2020

A complete introduction to the topic. Some interviews are lacking of audio and video quality. The assignments are pretty suitable to the content.

By Danish R

Oct 09, 2016

More information on Programming Assignment would have been helpful . Overall a good course to begin the specialization

By Atieno M S

Aug 16, 2019

The course was a good one with content that's understandable. I can't wait to proceed to the next one

By Wesley H

May 09, 2018

Great introduction to Recommender systems. Really got me thinking about how I could apply them.

By ignacio v

Feb 04, 2019

done it by audit, thnks!!! great stuff guys... but should do some practice in python!

By Reza N

Apr 27, 2017

The course was easy to understand. but i find the slides not much of help.

By Nitin P

Nov 18, 2016

I think this is a good course to start exploring recommendation systems.

By Ben C

Oct 30, 2017

I'd really like trying coding, but there's no Python option..

By Mehmet E

Jan 13, 2018

videos are too long... I had to watch them with x2 speed...

By Peter P

Oct 04, 2016

Too theoretical. I hope other parts will have more details.