Chevron Left
返回到 Introduction to Recommender Systems: Non-Personalized and Content-Based

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
531 个评分
110 条评论

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).

筛选依据:

76 - Introduction to Recommender Systems: Non-Personalized and Content-Based 的 100 个评论(共 106 个)

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 Aleshin A

May 18, 2018

It would be better to make practice on Python.

创建者 Andre C

Mar 30, 2020

Great course

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.

创建者 Chun-Huang L

Apr 06, 2020

The pace is too slow. Lectures spend lots of time on examples, and all kinds of possible variables.

These make stories very long, and badly-structured. It may be better to introduce only one concept at any moment, and discuss the problem and the solution immediately after mentioning the concept. That will help students to focus on the point and get it right sooner. It's good to combine all these concepts together after we've known everything, but not at the very beginning.

Also the programming assignment is really bad. As a CS student, I spent almost 90% of time on realizing the architecture, tools and libraries. I don't think these third-party libraries are helpful here. The same tasks can be implemented by pure Java code even more efficiently (for coding). Most non-CS students will find it difficult to use, while CS students can learn only little from the assignment since the core ideas to implement are far too easy.

I can feel how much knowledge lectures expect us to get from this lecture, but it really needs a rebuilding. Maybe trying to put a self limitation on video length will be a good start. Expressing a brief idea in a short video, and allowing students to consume one video even with only a piece of time, should be one of the most appealing part in flip-classroom.

创建者 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.

创建者 Maksym Z

Jan 30, 2017

Pros:

Some useful terminology if you want to ever communicate with someone who does recommender systems.

Cons:

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.

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.

创建者 Sachin S

Oct 31, 2016

I expected a lot from this course but it could have been a lot better - lengthy videos, not trying to explain the concepts in an understandable ways. Ended up confusing with various interviews and what are differences between various content based recommenders. The programming exercises were good and provided a good overview.

创建者 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.

创建者 Faizan A

Mar 01, 2017

The assignments are not very relevant to what is being taught. Java 7 instead of Java 8 makes things too verbose. Lenskit is painful to use and in the week 4 Honors assignment its just impossible to get the results desired by the grader. I would suggest the Teaching team to use R/python scikit instead of Java

创建者 Paulo E d V

Dec 08, 2016

Ok, it's an introduction, but it could at least show us some math or pseudocodes. A part from that, the course is really awesome. Well structured classes, good explanations and incredible interviews

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 Siddhartha S B

May 13, 2020

Honors track should be in Python. The subjective questions of the evaluation lacks clarity in some cases.

创建者 Artur K

Sep 12, 2017

The introduction is very slow in my opinion. Hopefully, it will pick up the pace in the later modules.

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 Ruth B

Aug 13, 2017

Not bad for an introduction, but I would have prefered it to be more technical

创建者 Lucas P B

Sep 04, 2019

Was expecting programming activities in Python or R, not in Java =/

创建者 Michael B

Dec 31, 2019

I feel like the course could've been condensed to 1 or 2 weeks max

创建者 AISHWARY B

Mar 29, 2020

Coding assignment should not be just restricted to java

创建者 MinhyePark

Feb 27, 2020

수학개념이 부족해서 조금 추상적으로 이해하게 되었습니다.