Chevron Left
Volver a Introduction to Recommender Systems: Non-Personalized and Content-Based

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

4.5
421 calificaciones
81 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).

Filtrar por:

26 - 50 de 78 revisiones para Introduction to Recommender Systems: Non-Personalized and Content-Based

por ignacio g

Oct 27, 2016

The course es really helpfull to understand how the recommender system works and what points yo have to take care when you have to implement

por Тефикова А Р

Oct 05, 2016

Курс очень понравился, спасибо большое за такую уникальную возможность вникнуть в суть рекомендательных систем!

por Daniel P

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).

por 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.

por Luis D F

Apr 17, 2017

Really good course to get started with recommendation systems!

por Shuang L

Nov 21, 2017

great professors and inspiring lectures!

por Patrick D

Jun 25, 2017

Great, thorough introduction with tracks for both Java programmers and non-programmers.

por Dan T

Oct 31, 2017

great overview of the breadth of material to get started

por Ashwin R

Jun 26, 2017

An excellent in-depth introduction into the concepts around recommendation systems!

por Yuncheng W

Nov 03, 2016

I think this is an amazing course for beginners who are interested in recommender systems, I strongly recommend this course to the students and engineers who are working on recommender systems.

por Rosni L

Oct 04, 2016

This course is really helpful in understanding the state of the art of non-personalized and content-based recommender systems. More it is invaluable to have changes to get the latest information from the expert through the interviews.

por Yury Z

Mar 08, 2018

Informative and helpfull for me as recommender systems practitioner. Even for things I've knew already the authors offer clean and holistic base. Surprisingly the honour track programming assignments was pretty challenging.

por vibhor n

Jun 03, 2019

A good introduction to the basic concepts of recommender systems. Loved the idea of having excel work assignments. For someone just wanting a quick learning of the concepts doesn't have to go through all the Java stuff

por Xinzhi Z

Jul 18, 2019

Great course. I really appreciated the efforts spent by the course team.

por Su L

Aug 23, 2019

great course, learnt a lot, thanks!

por LI Z

Jan 01, 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.

por

Feb 28, 2019

not so deep

por ignacio v

Feb 04, 2019

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

por Ankur S

Sep 25, 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

por Danish R

Oct 09, 2016

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

por Abhisek G

Jun 05, 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.

por Diana H

Jul 29, 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. :)

por Reza N

Apr 27, 2017

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

por scott t

Aug 03, 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.

por Peter P

Oct 04, 2016

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