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学生对 明尼苏达大学 提供的 Introduction to Recommender Systems: Non-Personalized and Content-Based 的评价和反馈

4.5
459 个评分
93 个审阅

课程概述

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....

热门审阅

BS

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.

DP

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).

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51 - Introduction to Recommender Systems: Non-Personalized and Content-Based 的 75 个评论(共 89 个)

创建者 Ankur S

Sep 25, 2018

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

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 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 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 Reza N

Apr 27, 2017

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

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 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 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 Wesley H

May 09, 2018

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

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 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 Ben C

Oct 30, 2017

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

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 Mehmet E

Jan 13, 2018

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

创建者 Aussie P

Jul 02, 2017

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.

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 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 Nitin P

Nov 18, 2016

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

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 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 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 Joeri K

Mar 23, 2019

It would be nice to have a hierarchical overview of the recommender systems. It's easy to get lost which is a subcategory of which. Thanks for the course!

创建者 Md. S R

Jan 05, 2019

The lecturer were very lengthy, at least for me. I find it difficult to concentrate.

创建者 Jon H

Feb 14, 2019

The content of this course is solid. It's a good introduction to content based and non-personailzed recommender systems. However, the presentation is poor. The course is largely based around videos which appear to be single takes. Snappier, well edited videos would have been better and, as a result, I often found myself skimming the transcripts rather than watching the videos.