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Отзывы учащихся о курсе Introduction to Recommender Systems: Non-Personalized and Content-Based от партнера Миннесотский университет

4.5
звезд
Оценки: 559
Рецензии: 118

О курсе

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....

Лучшие рецензии

BS
12 февр. 2019 г.

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 дек. 2017 г.

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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1–25 из 114 отзывов о курсе Introduction to Recommender Systems: Non-Personalized and Content-Based

автор: Ali K

2 янв. 2018 г.

well one thing I am struggling with programming in JAVA. Would not it be handy to have option to do assignment using languages like python/R? which are basically language of choice for data scientists and also easy to have grasp on for newbies. one more thing some time I just get stuck and felt like now way out. I did not get any answer/help form posts on the forum .

автор: Benjamin S S

12 февр. 2019 г.

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.

автор: Nicolás A

28 июня 2018 г.

Too basic and too repetitive (the videos could be half as long)

автор: Tash B

27 июня 2018 г.

Fantastic course. Lecturers have extensive experience in this field. Lectures include interviews with people who have successfully implemented recommender systems in their products or who are researching the permutations, challenges and extensions to recommender system development. Not only does the course provide the chance to build your own recommender systems (optional) but also highlights the complexities and opportunities for refining and improving recommendations. I highly recommend this course to anyone building recommendation systems.

автор: Seema P

7 янв. 2017 г.

Exceptional quality.The course content is comprehensive and practical enough applied at workplaces.

Guest lectures are super helpful and assignments are very practical yet make you think.

Thank you Coursera and Minnesota professors for this amazing course and wonderful opportunity for people like me with no background in recommendation systems learn the best research methods and practices in this field.

автор: Chun-Huang L

6 апр. 2020 г.

The pace is too slow. Lectures spend lots of time on examples, and all kinds of possible variables.

These make stories very long, and badly-structured. It may be better to introduce only one concept at any moment, and discuss the problem and the solution immediately after mentioning the concept. That will help students to focus on the point and get it right sooner. It's good to combine all these concepts together after we've known everything, but not at the very beginning.

Also the programming assignment is really bad. As a CS student, I spent almost 90% of time on realizing the architecture, tools and libraries. I don't think these third-party libraries are helpful here. The same tasks can be implemented by pure Java code even more efficiently (for coding). Most non-CS students will find it difficult to use, while CS students can learn only little from the assignment since the core ideas to implement are far too easy.

I can feel how much knowledge lectures expect us to get from this lecture, but it really needs a rebuilding. Maybe trying to put a self limitation on video length will be a good start. Expressing a brief idea in a short video, and allowing students to consume one video even with only a piece of time, should be one of the most appealing part in flip-classroom.

автор: Maksym Z

29 янв. 2017 г.

Pros:

Some useful terminology if you want to ever communicate with someone who does recommender systems.

Cons:

Very diluted content.

Mostly large text slides with the presenter talking in a monotone voice.

Programming exercises are done in Java and require deploying an IDE + an unused open source project developed by the authors. Hint to the authors: use Python, R or Octave like everyone does.

Some of the questionaries are ambiguous.

автор: Jon H

14 февр. 2019 г.

The content of this course is solid. It's a good introduction to content based and non-personailzed recommender systems. However, the presentation is poor. The course is largely based around videos which appear to be single takes. Snappier, well edited videos would have been better and, as a result, I often found myself skimming the transcripts rather than watching the videos.

автор: Sharat M

9 нояб. 2016 г.

As an introductory course, the content was good. But I wish the approach was more analytical and more hands on. Rather than history of Recommender systems & what happened in the 90s, I would have been happier if the course was able to throw light on the latest stuff in this field, the latest mathematical techniques etc.

автор: Faizan A

1 мар. 2017 г.

The assignments are not very relevant to what is being taught. Java 7 instead of Java 8 makes things too verbose. Lenskit is painful to use and in the week 4 Honors assignment its just impossible to get the results desired by the grader. I would suggest the Teaching team to use R/python scikit instead of Java

автор: Joeri K

23 мар. 2019 г.

It would be nice to have a hierarchical overview of the recommender systems. It's easy to get lost which is a subcategory of which. Thanks for the course!

автор: Siddhartha S B

13 мая 2020 г.

Honors track should be in Python. The subjective questions of the evaluation lacks clarity in some cases.

автор: Artur K

12 сент. 2017 г.

The introduction is very slow in my opinion. Hopefully, it will pick up the pace in the later modules.

автор: Md. S R

5 янв. 2019 г.

The lecturer were very lengthy, at least for me. I find it difficult to concentrate.

автор: Michael B

30 дек. 2019 г.

I feel like the course could've been condensed to 1 or 2 weeks max

автор: Oleg P

24 мая 2020 г.

There is no math in this course and it does not use Python. Therefore this course does a terrible job of preparing you for interview questions on Recommender systems. Personally I thought this course was a waste of my time and money. However the final excel exercise actually had some useful information, but it was only a 10 minute exercise after many hours of useless lectures. I could have done the same exercise for free.

автор: Alex B

25 авг. 2019 г.

This course mostly works. Contains a lot of wasted video time where no information is communicated. Uses simplistic tools that don't scale to data applications or otherwise dated tools not really used by data scientists or machine learning engineers making exercises either simplistic or a waste of time. Better than other courses in the series in that the assignments are legible.

автор: Timea K

2 июля 2017 г.

You should talk about music recommender systems as well! It was just OK, but boring some times... You were talking about lots of evident things by Amazon, making the course question. if it is seriously a university content.

автор: Elena G

4 нояб. 2020 г.

Someone really needs to edit this course. From the videos (most of them over 20 minutes long! and with content barely usable in the assignments) to the extraordinarily unclear English in the assignment texts... this course really needs a good cleaning up!

автор: Neha G

20 нояб. 2019 г.

would give negative rating if it was possible, course appears non-cohesive and dispersed without any clear terminology being used in the videos. Assignments are not clear either.

автор: Francisco R

7 июля 2020 г.

Info desactualizada y no tiene la opción de usar python

автор: Pham V H

11 дек. 2016 г.

the video is too long!

автор: Mehmet

15 окт. 2020 г.

Recommender systems have big impact in our digital life. In the past we trust acquaintance's opinions before buying, renting or watching. short time ago we handed over steering to the machines and algorithms. We trust their suggestions for watching, buying something, even driving a car. Therefore recommender systems will be top prior inevitable aspect for every organisation. This course is a brief introduction to Recommender System. I suggest everyone who interested in. Thank for Joseph Konstan and Michael Ekstrand

автор: Gurupratap S M

1 дек. 2019 г.

Really a very nice course with great attention to detail. The guest interviews were also superb and gave me exposure to different areas of research in recommender systems in general. Both Michael and Joe are experts and provide deep insights with plenty of examples and study cases. Honors exercises are another added bonus to practice and get hands on experience. I had already deployed a recommender system in production am glad to continue learning and learn different techniques. Thank you once again

автор: Nesreen S

14 нояб. 2019 г.

I found this course very informative. with real-life examples of the recommender's use case and who it can be implemented. I loved that it has an excel assignment to get an intuition about the concepts allowing business-like and non-techincal audiences to understand and practice the concepts. I found the honor track and assignment though challenging but very important and helpful though the documentation of lenskit was not very clear.

it was enjoyable and very useful.