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Bewertung und Feedback des Lernenden für Introduction to Recommender Systems: Non-Personalized and Content-Based von University of Minnesota

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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....

Top-Bewertungen

BS

12. Feb. 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

7. Dez. 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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126 - 128 von 128 Bewertungen für Introduction to Recommender Systems: Non-Personalized and Content-Based

von Priyamvada S

6. Dez. 2021

doesnt cover collaborative; rest is fine

von ­박민혜 / 학 / 데

26. Feb. 2020

수학개념이 부족해서 조금 추상적으로 이해하게 되었습니다.

von Roman O

5. Nov. 2021

Bad, pretty bad. Too theoretical. I've got felling all the time that a LOT of terms I hearing first time and felling that they was spoken somewhere else, but not here. It is horrible frankenstein of scattered knowledge that lectors pretended to call a 'cource'. I've got more knowledge just from reading few chapters on recomendation systems book, than listening to this.