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Matrix Factorization and Advanced Techniques に戻る

ミネソタ大学(University of Minnesota) による Matrix Factorization and Advanced Techniques の受講者のレビューおよびフィードバック

4.3
177件の評価
26件のレビュー

コースについて

In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders....

人気のレビュー

HL
2021年1月2日

Really enjoyed the course!\n\nOne suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)

LL
2017年7月18日

great courses! They invite a lot of interviews to let me understand the sea of recommend system!

フィルター:

Matrix Factorization and Advanced Techniques: 1 - 25 / 25 レビュー

by Daniel P

2018年1月7日

Very interesting topic which I was really stoked to learn but unfortunately this course is missing the deep insight into the algorithms, it explains just one algorithm and its variations. The content is overall to little for 6 weeks course and the honor's assignment has very bad task description with errors and lack of validating possibilities.

by Evaristo C

2017年9月17日

This is THE BEST course in Recommendation Systems. Probably getting a bit outdated as the field is moving rapidly into deep learning and other techniques, but the problems faced by any recommendation system developer would be the same - ratings measurement, sparse matrices, ranking metrics... And THAT is probably the best contribution of the whole training: not only talking about the methods but pin-pointing the main REAL problems to solve.

REALLY GOOD!!!!!!!

by Ankur S

2019年1月26日

Great course to understand the fundamentals of recommender systems, as well the diversity & challenges of different recommender systems. The interviews with people from relevant academic fields & industry were particularly useful

I really wish that the programming exercises would be in Python. And that more details on how to implement them (the actual modeling of the algorithms etc) was delved into greater detail.

by Su L

2019年9月11日

It will be great, if we can do honor's track with Python or R

by Bret

2017年6月16日

The topics that were covered were done well, but I was expecting (and hoping for) way more on probabilistic matrix factorization (I still struggle to understand how it differs from regular MF) and Boltzmann machines. Also, only one assignment in the standard track? And you couldn't auto-grade standard track assignments and quizzes without charging a fee? Weak

by Nicolás A

2018年7月24日

-some videos need better editing

-should go into more mathematical detail for the matrix factorization techniques

by Daniel M

2019年8月31日

Content okay, if a little basic. Second honor's track assignment is a complete mess; can't even build properly. Complaints on discussion board have gone unanswered for months.

by Amine D

2017年12月27日

the way of explaining every concept is very boring

by Keshaw S

2018年3月7日

Based on my experience with the previous courses in this specialization, I was very positively surprised by the amount and depth of material provided in this course. It covers almost everything that is there to be known. The comprehensive interviews are a big plus point. Also, this course provides guidelines as to how to develop, employ and evaluate a recommender system in real life. I would definitely recommend anyone interested in the field to take this course.

by Blake C

2018年1月13日

Great course. Professors do an excellent job of breaking down this stuff into digestible bits without losing much substance. Highly recommend. You'll need to do some outside exploration and learning though.

by Hagay L

2021年1月3日

Really enjoyed the course!

One suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)

by Light0617

2017年7月18日

great courses! They invite a lot of interviews to let me understand the sea of recommend system!

by Joeri K

2019年3月27日

Great to have people from industry to talk about recommenders as well. Thanks a lot!

by Sanjay K

2017年12月4日

Awesome course especially for those doing Ph.D in recommender systems

by Ziling C

2020年3月29日

so very helpful!

by Rahul G

2018年2月19日

The Hybrid recommenders i.e. Week 4 needs more explanation especially what is Tensor factorization etc, Week 4 was difficult to grasp. Week 5 and Week 6 was informative especially the LinkedIn video and Learning to Rank: Interview with Xavier Amatriain in which a problem was discussed of having that popularity vs Ranking issue.

Thanks it was quite a lot new things to learn ! My Rating 4.5

by Dennis D

2021年1月10日

Very good. Per closing comments, it probably needs an update (since 2016) as this is active, progressive area.

by Nora P H L

2020年4月23日

The content is really good, but overall the interviews with experts in the field are the best of this course.

by Alberto G

2018年6月10日

Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.

by aankitakaur

2017年8月14日

Interview with Francesco Ricci

is very knowledgeable about context aware Recommender System.

by LU W

2018年8月25日

It would be better if explaining how to build latent features

by Siwei Y

2017年6月29日

内容还是蛮有意思的。 就是 Interview 太多, 而且interview 的人的 上课水平参差不齐, 所以干货 不多。 另外 希望honor assignment 的 参考结果 应该比较 reproducible, 否则很难知道自己的 code 错在哪里。

Content is really interesting, But there are too much interviews, to give students more systematical impression. Besides that , I hope that the example output of honor assigment should be more reproducible , otherwise it is hardly to know if our code is corrrect.

by Kemal C K

2017年12月28日

Few quizes. Easy tests. We need more information on the core techniques.

by Moustafa M

2020年4月18日

The HWs for the Honor track had mistakes

by PRATIK K C

2020年6月9日

Could have been better