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Bewertung und Feedback des Lernenden für Introduction to Recommender Systems: Non-Personalized and Content-Based von University of Minnesota

624 Bewertungen

Über den Kurs

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....



12. Feb. 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.


7. Dez. 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 von 130 Bewertungen für Introduction to Recommender Systems: Non-Personalized and Content-Based

von rahul r

9. Juni 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.

von shailesh k p

22. Juni 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 and explaining content based and collaborative filtering is easy to grasp.

von Diana H

29. Juli 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. :)

von nitish a

7. Apr. 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

von Lucas B A d A

3. Apr. 2020

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

von Danish R

9. Okt. 2016

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

von Atieno M S

16. Aug. 2019

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

von Wesley H

9. Mai 2018

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

von ignacio v

4. Feb. 2019

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

von Lalu P L

19. Sep. 2022

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

von Reza N

27. Apr. 2017

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

von Nitin P

18. Nov. 2016

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

von Ben C

30. Okt. 2017

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

von Mehmet E

13. Jan. 2018

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

von Peter P

4. Okt. 2016

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

von Aleshin A

18. Mai 2018

It would be better to make practice on Python.

von Egbert R

11. Apr. 2021

Great course.

von Andre C

30. März 2020

Great course

von Gabriel S

28. Feb. 2019

not so deep

von Chunyang S

3. Feb. 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.

von Akash S C

22. Juni 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.

von Sachin S

31. Okt. 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.

von Paulo E d V

8. Dez. 2016

Ok, it's an introduction, but it could at least show us some math or pseudocodes. A part from that, the course is really awesome. Well structured classes, good explanations and incredible interviews

von Yan F

19. Sep. 2021

The course was generally ok, but can benefit from better lecture structure. For example, the general topic can move upfront, with more mathematical illustration on how content filtering is done.

von Ruth B

13. Aug. 2017

Not bad for an introduction, but I would have prefered it to be more technical