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

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129 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....

Top-Bewertungen

BS

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.

DP

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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76 - 100 von 128 Bewertungen für Introduction to Recommender Systems: Non-Personalized and Content-Based

von Garvit G

22. März 2018

awesome course.

von Manikant R

21. Juni 2020

Great course

von jonghee

28. Okt. 2019

good lecture

von Mustafa S

8. Feb. 2019

Great course

von 20PH0516 S P

26. Sep. 2019

Nice course

von Muhammad Z H

17. Sep. 2019

Learnt alot

von 姚青桦

16. Okt. 2017

Pretty good

von Bahri E S

30. Mai 2022

Perfect

von HN M

28. Aug. 2017

great!

von Rafael A H P

1. Juli 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.

von Raffaele Z

1. Juli 2022

I liked this course. A lot, actually. I liked the business focus. Only, I didn't give 5 stars because I think that the course would benefit from some more details about the mathematical details of the content-based recommender (it's just some linear algebra). I mean, I think it is good and inclusive to keep things simple, but at the end of each week, a slightly more formal section, like the one at the end of the course (about the unified approach bla bla bla), would be great, especially for those more mathematically oriented. Anyway, great course

von Taaniya A

19. März 2022

Very insightful and concise way of teaching the concepts. The interviews with experts from each area was very helpful learn the concepts from application perspective & to formulate real world problems & apply these concepts there.

Would have given 5 stars if the programming assignments were also in python.

I never skip programming assignments but this time I had to which is very disappointing.

Please upgrade the couurse to let students learn them with languages of their choice.

Thanks,

Taaniya Arora

von TOM C

19. Apr. 2020

The two teachers were very good, the interviews were quite interesting, the assignments were well built in order to better understand the course. I'm a bit disappointed, I was thinking to do more maths or code with classical languages such as Python or R. I never used Java and I didn't want to download a new software to start coding in Java. Maybe I should take a look to the Honor program even if I don't know anything about Java...

Thanks for all !

von Ankur S

25. Sep. 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

von Alejo P

13. Sep. 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.

von Jan Z

20. Okt. 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.

von Keshaw S

2. Feb. 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.

von Hagay L

16. Juni 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.

von LI Z

1. Jan. 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.

von Elias A H

22. Nov. 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.

von scott t

3. Aug. 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.

von Dhananjay G

21. Dez. 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.

von Swetha P S

25. Okt. 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.

von Abhisek G

5. Juni 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.

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.