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Introduction to Recommender Systems: Non-Personalized and Content-Based に戻る

ミネソタ大学(University of Minnesota) による Introduction to Recommender Systems: Non-Personalized and Content-Based の受講者のレビューおよびフィードバック

4.5
607件の評価
128件のレビュー

コースについて

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems....

人気のレビュー

BS

2019年2月12日

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.

DP

2017年12月7日

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).

フィルター:

Introduction to Recommender Systems: Non-Personalized and Content-Based: 76 - 100 / 126 レビュー

by Garvit G

2018年3月22日

awesome course.

by Manikant R

2020年6月21日

Great course

by jonghee

2019年10月28日

good lecture

by Mustafa S

2019年2月8日

Great course

by 20PH0516 S P

2019年9月26日

Nice course

by Muhammad Z H

2019年9月17日

Learnt alot

by 姚青桦

2017年10月16日

Pretty good

by HN M

2017年8月28日

great!

by Rafael A H P

2017年7月1日

Well prepared course. In-depth lecture. Easy to follow even when listening only. The course lectures is very detailed, and that is one thing I really liked. The videos does feel a bit long, and maybe we can chop it to smaller sub-topics.

The interviews are very interesting and show a glimpse of broader universe of recommendation system. However, the concepts explained in the interview is a bit hard to follow, as there is no accompanying presentation materials and it jumps to detailed content with little context

The regular exercise feels very easy but helpful to make the concepts concrete. The Honors programming exercise looks interesting & challenging, but it seems too hard for someone with no programming background. I am also learning Python in parallel, so I decided to drop it to avoid learning 2 languages in parallel.

by Taaniya A

2022年3月19日

Very insightful and concise way of teaching the concepts. The interviews with experts from each area was very helpful learn the concepts from application perspective & to formulate real world problems & apply these concepts there.

Would have given 5 stars if the programming assignments were also in python.

I never skip programming assignments but this time I had to which is very disappointing.

Please upgrade the couurse to let students learn them with languages of their choice.

Thanks,

Taaniya Arora

by TOM C

2020年4月19日

The two teachers were very good, the interviews were quite interesting, the assignments were well built in order to better understand the course. I'm a bit disappointed, I was thinking to do more maths or code with classical languages such as Python or R. I never used Java and I didn't want to download a new software to start coding in Java. Maybe I should take a look to the Honor program even if I don't know anything about Java...

Thanks for all !

by Ankur S

2018年9月25日

Very informative, very well organized. Especially like the questions like "Which domain would this technique most likely to apply".

Some areas of improvement to consider

The overall pace of the content delivery in various lectures could be increased. Tends to get very slow at times

More hands on exercises would be useful

Programming exercise in Python or Python based frameworks would bee useful

by Alejo P

2019年9月13日

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.

by Jan Z

2016年10月20日

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.

by Keshaw S

2018年2月2日

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.

by Hagay L

2019年6月16日

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.

by LI Z

2019年1月1日

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.

by Elias A H

2016年11月22日

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.

by scott t

2017年8月3日

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.

by Dhananjay G

2019年12月21日

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.

by Swetha P S

2017年10月25日

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.

by Abhisek G

2017年6月5日

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.

by rahul r

2018年6月9日

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.

by shailesh k p

2018年6月22日

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.

by Diana H

2017年7月29日

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. :)