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Introduction to Recommender Systems: Non-Personalized and Content-Based, Université du Minnesota

4.5
388 notes
76 avis

À propos de ce cours

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....

Meilleurs avis

par 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.

par 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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72 avis

par shayue

Apr 11, 2019

Really Good! I think it will be helpful to me and take a job for me!

par Joeri Kiekens

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!

par

Feb 28, 2019

not so deep

par Jon Holdship

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.

par Benjamin S. Skrainka

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.

par Mustafa SENHAJI

Feb 08, 2019

Great course

par ignacio vilieri

Feb 04, 2019

done it by audit, thnks!!! great stuff guys... but should do some practice in python!

par Mai Hong Son

Jan 20, 2019

good exercises & lectures

par Md. Shamsur Rahim

Jan 05, 2019

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

par LI ZONGXI

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.