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Opiniones y comentarios de aprendices correspondientes a Introduction to Recommender Systems: Non-Personalized and Content-Based por parte de Universidad de Minnesota

4.5
estrellas
481 calificaciones
96 revisiones

Acerca del Curso

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....

Principales revisiones

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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76 - 92 de 92 revisiones para Introduction to Recommender Systems: Non-Personalized and Content-Based

por Akash S C

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.

por 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.

por 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.

por Sachin S

Oct 31, 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.

por 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.

por 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

por Paulo E d V

Dec 08, 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

por 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!

por Artur K

Sep 12, 2017

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

por Md. S R

Jan 05, 2019

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

por Ruth B

Aug 13, 2017

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

por Lucas P B

Sep 04, 2019

Was expecting programming activities in Python or R, not in Java =/

por Michael B

Dec 31, 2019

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

por 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.

por 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.

por 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.

por andrew

Dec 12, 2016

the video is too long!