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学生对 明尼苏达大学 提供的 Introduction to Recommender Systems: Non-Personalized and Content-Based 的评价和反馈

4.5
549 个评分
116 条评论

课程概述

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....

热门审阅

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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1 - Introduction to Recommender Systems: Non-Personalized and Content-Based 的 25 个评论(共 112 个)

创建者 Ali K

Jan 02, 2018

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 .

创建者 Benjamin S S

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.

创建者 Nicolás A

Jun 28, 2018

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

创建者 Tash B

Jun 27, 2018

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.

创建者 Seema P

Jan 07, 2017

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.

创建者 Chun-Huang L

Apr 06, 2020

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.

创建者 Maksym Z

Jan 30, 2017

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.

创建者 Jon H

Feb 14, 2019

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.

创建者 Sharat M

Nov 09, 2016

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.

创建者 Faizan A

Mar 01, 2017

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

创建者 Joeri K

Mar 23, 2019

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!

创建者 Siddhartha S B

May 13, 2020

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

创建者 Artur K

Sep 12, 2017

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

创建者 Md. S R

Jan 05, 2019

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

创建者 Michael B

Dec 31, 2019

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

创建者 Oleg P

May 24, 2020

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.

创建者 Alex B

Aug 26, 2019

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.

创建者 Timea K

Jul 02, 2017

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.

创建者 Elena G

Sep 11, 2020

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!

创建者 Neha G

Nov 20, 2019

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.

创建者 Francisco R

Jul 07, 2020

Info desactualizada y no tiene la opción de usar python

创建者 andrew

Dec 12, 2016

the video is too long!

创建者 Gurupratap S M

Dec 02, 2019

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

创建者 Nesreen S

Nov 14, 2019

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.

创建者 sidra n

Aug 15, 2018

I would like to have more detail and help for honors track especially for people like me who do not have much programming experience and want to learn how to implement recommender system. I am unable to solve the assignment and i still need some help. Would be great if the solutions of the honors track should be available to those who want to learn and not just for the sake of getting certificate