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.

I think some of the interviews didn't really give me great insights. I know this is only an introduction, but I was expecting more fields than movies. I am overly critical though, all in all a very good way to understand recommendation systems.

I am very new to recommendation system and yet able to comprehend the lessons. The best thing is explaining the system with example. Walking through Amazon.com and explaining content based and collaborative filtering is easy to grasp.

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. :)

The course and its content was quite interesting and easy, so I will be taking the next course in this specialization of Recommender System Specialization

A complete introduction to the topic. Some interviews are lacking of audio and video quality. The assignments are pretty suitable to the content.

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

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

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

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

Please update the specialization, it's 2022, and the course slides are from 2016.

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

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

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

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

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

It would be better to make practice on Python.

Would've liked honor to be in Python

Great course.

Great course

not so deep

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.

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.

Lectures were Ok. Might benefit from more visual ques.

However, I strongly didn't how assignments were presented. They were ordered so that someone will start reading and completing them, and once that's done, the next few "modules" are about explaining what will be done in the assignment. That's both for normal and honor track.

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.