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

4.4
stars
637 ratings

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 12, 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 7, 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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101 - 125 of 134 Reviews for Introduction to Recommender Systems: Non-Personalized and Content-Based

By Abhisek G

•

Jun 5, 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

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Jun 9, 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

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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 Diana H

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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

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Apr 7, 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

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Apr 3, 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

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Oct 9, 2016

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

By Atieno M S

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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

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May 9, 2018

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

By ignacio v

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Feb 4, 2019

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

By Lalu P L

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Sep 19, 2022

Please update the specialization, it's 2022, and the course slides are from 2016.

By Reza N

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Apr 27, 2017

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

By Nitin P

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Nov 18, 2016

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

By Ben C

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Oct 29, 2017

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

By Mehmet E

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Jan 13, 2018

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

By Peter P

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Oct 4, 2016

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

By Aleshin A

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May 18, 2018

It would be better to make practice on Python.

By Aladdin P

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Oct 18, 2023

Would've liked honor to be in Python

By Egbert R

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Apr 11, 2021

Great course.

By Andre C

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Mar 30, 2020

Great course

By Gabriel S

•

Feb 28, 2019

not so deep

By Chunyang S

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Feb 3, 2017

Generally I like the contents of this course. I particularly like that insights are provided in terms of what aspects to consider when designing a recommender system; pros and cons of different approaches. However I'm also extremely bored watching the videos because looking at the lectures reading the scripts (most of the time with very slow speed) is one of the quickest way to send people to sleep. I'd hope the lectures will improve their presenting skills.

Another comment is the honours track assignments should really be put into more thoughts. I passed them with 100% credit, but I didn't feel I gained a lot useful knowledge through this exercise. Generally it felt to me that the complexity of the implementation is much much more than needed in relation to the complexity of the problems. Eventually this assignment became grinding with Java's verbose, annoying syntax and unnecessary computations designed in lab instruction. For example, in the first programming assignment, why if the ModelProvider object already computed the entire map of ratings, and the map is directly needed in the Recommender object, the Model object only provide API to retrieve individual rating but not the entire map?! Isn't it a wasteful computation to reconstruct the rating map? So I doubt the structural design of the program is sensible, or the expected solution would actually be done in real applications. Also I think Java is just a really out-dated, bulky language to work with in this kind of task. It really makes the assignment experience awful.

By Akash S C

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Jun 22, 2019

Good course for basic intro to recommender system. However, some basic problems - videos are too long and Java for programming assignment was a huge disappointment. i tried picking the lenskit assignment with java but decided to get rid of it and replicated the assignment in python instead. it was taking too much time to learn Java back which will never be used in regular work for data science. python or R should have been used for prog assignment. time to update the course.

By Dawid L

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Jun 28, 2023

Lectures were Ok. Might benefit from more visual ques.

However, I strongly didn't how assignments were presented. They were ordered so that someone will start reading and completing them, and once that's done, the next few "modules" are about explaining what will be done in the assignment. That's both for normal and honor track.

By Sachin S

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Oct 30, 2016

I expected a lot from this course but it could have been a lot better - lengthy videos, not trying to explain the concepts in an understandable ways. Ended up confusing with various interviews and what are differences between various content based recommenders. The programming exercises were good and provided a good overview.