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.
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).
创建者 Alejo P•
Sep 13, 2019
The course is really well oriented, topics are broadly covered with good explanations and examples. One major drawback of this course is that the honors track is not implemented in Python, though I believe that possibly in future versions this will be adapted. In my case, the two options left are either I learn Java programming or I do not take the honors track.
创建者 Jan Z•
Oct 20, 2016
The course authors did a great job explaining concepts related to recommender systems. However, the programming assignments require Java usage, even though they could easily allow people to use different software, by just explaining the required algorithm and accepting a csv file with orderings/predictions. That was quite disappointing.
创建者 Keshaw S•
Feb 02, 2018
Some of the assignments are not particularly well created, in the sense that they seem to emphasize on recalling rather than learning, Also, most of the interview failed to hold my attention in general.
Overall, however, this is a very good course and gives a comprehensive overview of the prevalent techniques in the relevant fields.
创建者 Hagay L•
Jun 16, 2019
Overall a good course that teaches the basics for content based recommenders.
Would be great if the assignments were a bit more challenging, e.g.: work with large datasets (and not the tiny datasets used in the assignments)
Would also be good if we were provided papers of recent/notable research on the topic to read further.
创建者 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.
创建者 Abou-Haydar E•
Nov 22, 2016
I love the course's content but discussions are of poor quality and the honors tracks assignments are a little messy. I ought having more explanation about the tool to use or maybe doing the programming assignments in another tool/language than Lenskit even it seems like a decent project.
创建者 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.
创建者 Dhananjay G•
Dec 21, 2019
I found this course very useful for me to get in to basics and back ground of recommendations. Each topic is presented and discussed quite in detail . I also found the interviews with various expert in Recommendations very insightful. Thanks you Joe and Micheal.
创建者 Swetha P S•
Oct 25, 2017
Very informative course! I had a great learning experience working on the programming assignments required for honors. The only drawback is the style of communication (written and spoken) is elaborate and confuses many non-native English speakers including me.
创建者 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.
创建者 Rahul R•
Jun 10, 2018
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.
创建者 shailesh k p•
Jun 22, 2018
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.
创建者 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. :)
创建者 Danish R•
Oct 09, 2016
More information on Programming Assignment would have been helpful . Overall a good course to begin the specialization
创建者 Atieno M S•
Aug 16, 2019
The course was a good one with content that's understandable. I can't wait to proceed to the next one
创建者 Wesley H•
May 09, 2018
Great introduction to Recommender systems. Really got me thinking about how I could apply them.
创建者 ignacio v•
Feb 04, 2019
done it by audit, thnks!!! great stuff guys... but should do some practice in python!
创建者 Reza N•
Apr 27, 2017
The course was easy to understand. but i find the slides not much of help.
创建者 Nitin P•
Nov 18, 2016
I think this is a good course to start exploring recommendation systems.
创建者 Ben C•
Oct 30, 2017
I'd really like trying coding, but there's no Python option..
创建者 Mehmet E•
Jan 13, 2018
videos are too long... I had to watch them with x2 speed...
创建者 Peter P•
Oct 04, 2016
Too theoretical. I hope other parts will have more details.
创建者 Алешин А Е•
May 18, 2018
It would be better to make practice on Python.
创建者 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.