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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



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....




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.



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).


Introduction to Recommender Systems: Non-Personalized and Content-Based: 26 - 50 / 126 レビュー

by Timea K


You should talk about music recommender systems as well! It was just OK, but boring some times... You were talking about lots of evident things by Amazon, making the course question. if it is seriously a university content.

by Neha G


would give negative rating if it was possible, course appears non-cohesive and dispersed without any clear terminology being used in the videos. Assignments are not clear either.

by Francisco R


Info desactualizada y no tiene la opción de usar python

by Pham V H


the video is too long!

by Mehmet


Recommender systems have big impact in our digital life. In the past we trust acquaintance's opinions before buying, renting or watching. short time ago we handed over steering to the machines and algorithms. We trust their suggestions for watching, buying something, even driving a car. Therefore recommender systems will be top prior inevitable aspect for every organisation. This course is a brief introduction to Recommender System. I suggest everyone who interested in. Thank for Joseph Konstan and Michael Ekstrand

by Gurupratap S M


Really a very nice course with great attention to detail. The guest interviews were also superb and gave me exposure to different areas of research in recommender systems in general. Both Michael and Joe are experts and provide deep insights with plenty of examples and study cases. Honors exercises are another added bonus to practice and get hands on experience. I had already deployed a recommender system in production am glad to continue learning and learn different techniques. Thank you once again

by Nesreen S


I found this course very informative. with real-life examples of the recommender's use case and who it can be implemented. I loved that it has an excel assignment to get an intuition about the concepts allowing business-like and non-techincal audiences to understand and practice the concepts. I found the honor track and assignment though challenging but very important and helpful though the documentation of lenskit was not very clear.

it was enjoyable and very useful.

by sidra n


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

by Shantanu B


This course takes me through many of the techniques that started at the dawn of recommendation systems and some which are still going strong in certain domains and certain scale. Rather than just concentrating on the numerical aspects of the topic, there has been a great emphasis on learning the tricks of the trade and the aspects that should be kept in mind while employing the techniques.

by muffaddal q


a good course with detail explanation on many aspect of non-personalized and content based recommendations. Interviews with experts with excellent. Helped to learn how professionals are solving different problems related to recommendations in their respective fields.

by Julia K


This course is a wonderful logical informative introduction to several basic types of recommender systems. It is a great part to start! The instructors a clear and well organized. Some assignments were a little bit awkward but overall they

by Rosni L


This course is really helpful in understanding the state of the art of non-personalized and content-based recommender systems. More it is invaluable to have changes to get the latest information from the expert through the interviews.

by Yury Z


Informative and helpfull for me as recommender systems practitioner. Even for things I've knew already the authors offer clean and holistic base. Surprisingly the honour track programming assignments was pretty challenging.

by vibhor n


A good introduction to the basic concepts of recommender systems. Loved the idea of having excel work assignments. For someone just wanting a quick learning of the concepts doesn't have to go through all the Java stuff

by Mario W


Great lecture with smaller exercises, fully met my expectations. Excellent lecturers with for me perfect understandable English, interesting interviews and wrap-ups. Congratulations and many thanks!

by Yuncheng W


I think this is an amazing course for beginners who are interested in recommender systems, I strongly recommend this course to the students and engineers who are working on recommender systems.

by Danilo L A


Awesome. All concepts were very well explained in an understandable and didatical language.

Loved the interviews with all the specialists.

I've learned so much, thanks for this course!

by Daniel P


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).

by Igor P


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.

by Sonia F R


Un profesor excelente y un temario muy bueno. También me han gustado mucho las entrevistas y los recorridos por las páginas web que tienen recomendadores.

by Dame N


Thank you for your course, very Helpfull for those who are keep in touch with recommender System engine. This is a very cool Introduction course.

by Pawel S


As a software engineer with computer science background I found that course enhancing my knowledge. I'm going to continue the specialization.

by Ignacio G


The course es really helpfull to understand how the recommender system works and what points yo have to take care when you have to implement

by tao L


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

by Francisco C


Excelente curso, presenta una vista amplia de técnicas para la implementación de sistemas de recomendación, lo recomiendo totalmente.