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Introduction to Recommender Systems: Non-Personalized and Content-Based, University of Minnesota

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66 个审阅

课程信息

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

创建者 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).

创建者 IP

Sep 19, 2016

it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.

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62 个审阅

创建者 sagar srinivas

Oct 04, 2018

Awesome. Worth it!

创建者 Ankur Shrivastav

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 sidra naveed

Aug 15, 2018

I would like to have more detail and help for honors track especially for people like me who do not have much programming experience and want to learn how to implement recommender system. I am unable to solve the assignment and i still need some help. Would be great if the solutions of the honors track should be available to those who want to learn and not just for the sake of getting certificate

创建者 tao Li

Jul 22, 2018

I think I am on the right track to changing my career from java engineer from data scientist, this course is one of the best start point

创建者 Nicolás Aramayo

Jun 28, 2018

Too basic and too repetitive (the videos could be half as long)

创建者 Tash Bickley

Jun 27, 2018

Fantastic course. Lecturers have extensive experience in this field. Lectures include interviews with people who have successfully implemented recommender systems in their products or who are researching the permutations, challenges and extensions to recommender system development. Not only does the course provide the chance to build your own recommender systems (optional) but also highlights the complexities and opportunities for refining and improving recommendations. I highly recommend this course to anyone building recommendation systems.

创建者 shailesh kumar pandey

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 Rahul Ranjan

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 Алешин Алексей Евгеньевич

May 18, 2018

It would be better to make practice on Python.

创建者 Wesley Hutchinson

May 09, 2018

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