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.
Sep 19, 2016
it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.
von 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.
von Ruth B•
Aug 13, 2017
Not bad for an introduction, but I would have prefered it to be more technical
von Maksym Z•
Jan 30, 2017
Some useful terminology if you want to ever communicate with someone who does recommender systems.
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.
von Chunyang S•
Feb 03, 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.
von 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.
von Lucas P B•
Sep 04, 2019
Was expecting programming activities in Python or R, not in Java =/
von 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.
von 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.
Dec 12, 2016
the video is too long!