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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
596 calificaciones
124 reseña

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 reseñas

BS
12 de feb. de 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 de dic. de 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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101 - 122 de 122 revisiones para Introduction to Recommender Systems: Non-Personalized and Content-Based

por Danish R

9 de oct. de 2016

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

por Atieno M S

16 de ago. de 2019

The course was a good one with content that's understandable. I can't wait to proceed to the next one

por Wesley H

9 de may. de 2018

Great introduction to Recommender systems. Really got me thinking about how I could apply them.

por ignacio v

4 de feb. de 2019

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

por Reza N

27 de abr. de 2017

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

por Nitin P

18 de nov. de 2016

I think this is a good course to start exploring recommendation systems.

por Ben C

30 de oct. de 2017

I'd really like trying coding, but there's no Python option..

por Mehmet E

13 de ene. de 2018

videos are too long... I had to watch them with x2 speed...

por Peter P

4 de oct. de 2016

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

por Aleshin A

18 de may. de 2018

It would be better to make practice on Python.

por Egbert R

11 de abr. de 2021

Great course.

por Andre C

30 de mar. de 2020

Great course

por Gabriel S

28 de feb. de 2019

not so deep

por Chunyang S

3 de feb. de 2017

Generally I like the contents of this course. I particularly like that insights are provided in terms of what aspects to consider when designing a recommender system; pros and cons of different approaches. However I'm also extremely bored watching the videos because looking at the lectures reading the scripts (most of the time with very slow speed) is one of the quickest way to send people to sleep. I'd hope the lectures will improve their presenting skills.

Another comment is the honours track assignments should really be put into more thoughts. I passed them with 100% credit, but I didn't feel I gained a lot useful knowledge through this exercise. Generally it felt to me that the complexity of the implementation is much much more than needed in relation to the complexity of the problems. Eventually this assignment became grinding with Java's verbose, annoying syntax and unnecessary computations designed in lab instruction. For example, in the first programming assignment, why if the ModelProvider object already computed the entire map of ratings, and the map is directly needed in the Recommender object, the Model object only provide API to retrieve individual rating but not the entire map?! Isn't it a wasteful computation to reconstruct the rating map? So I doubt the structural design of the program is sensible, or the expected solution would actually be done in real applications. Also I think Java is just a really out-dated, bulky language to work with in this kind of task. It really makes the assignment experience awful.

por Akash S C

22 de jun. de 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 Sachin S

31 de oct. de 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 Paulo E d V

8 de dic. de 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 Yan F

19 de sep. de 2021

The course was generally ok, but can benefit from better lecture structure. For example, the general topic can move upfront, with more mathematical illustration on how content filtering is done.

por Ruth B

13 de ago. de 2017

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

por Lucas P B

4 de sep. de 2019

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

por ­박민혜 / 학 / 데

26 de feb. de 2020

수학개념이 부족해서 조금 추상적으로 이해하게 되었습니다.

por Roman O

5 de nov. de 2021

Bad, pretty bad. Too theoretical. I've got felling all the time that a LOT of terms I hearing first time and felling that they was spoken somewhere else, but not here. It is horrible frankenstein of scattered knowledge that lectors pretended to call a 'cource'. I've got more knowledge just from reading few chapters on recomendation systems book, than listening to this.