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.
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 Danish R•
More information on Programming Assignment would have been helpful . Overall a good course to begin the specialization
por Atieno M S•
The course was a good one with content that's understandable. I can't wait to proceed to the next one
por Wesley H•
Great introduction to Recommender systems. Really got me thinking about how I could apply them.
por ignacio v•
done it by audit, thnks!!! great stuff guys... but should do some practice in python!
por Reza N•
The course was easy to understand. but i find the slides not much of help.
por Nitin P•
I think this is a good course to start exploring recommendation systems.
por Ben C•
I'd really like trying coding, but there's no Python option..
por Mehmet E•
videos are too long... I had to watch them with x2 speed...
por Peter P•
Too theoretical. I hope other parts will have more details.
por Aleshin A•
It would be better to make practice on Python.
por Egbert R•
por Andre C•
por Gabriel S•
not so deep
por Chunyang S•
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•
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•
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•
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•
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•
Not bad for an introduction, but I would have prefered it to be more technical
por Lucas B•
Was expecting programming activities in Python or R, not in Java =/
por Priyamvada S•
doesnt cover collaborative; rest is fine
por 박민혜 / 학 / 데•
수학개념이 부족해서 조금 추상적으로 이해하게 되었습니다.
por Roman O•
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.