Introduction to Recommender Systems: Non-Personalized and Content-Based

En este programa aprenderAs a diseAar y a crear videojuegos en 2D y 3D y conocerAs el mercado donde se moverAn tus productos cuando estAn acabados. DominarAs los principios del diseAo y la arquitectura de los videojuegos, gestiAn de assets, animaciAn y publicaciAn. AdquirirAs una visiAn prActica sobre la industria del videojuego, y examinarAs estrategias efectivas de desarrollo de videojuegos. TambiAn explorarAs temas avanzados como el desarrollo de shaders y la optimizaciAn de videojuegos. En el programa usarAs el motor de videojuegos Unity y desarrollarAs hasta tres prototipos de videojuegos

( 172 Reviews )

Created by: Joseph A Konstan

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Overall Score : 86 / 100

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Course Description

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.

Time: 16h

Instructor Details

Joseph A Konstan

4.3 Rating
172 Reviews

Joseph A. Konstan is Distinguished McKnight University Professor and Distinguished University Teaching Professor of Computer Science and Engineering at the University of Minnesota. His research addresses a variety of human-computer interaction issues, including recommender systems, social computing, and applications of computing to public health. His work on the GroupLens Recommender System won the 2010 ACM Software Systems Award. Professor Konstan has been recognized for his teaching through both University and College teaching awards. He has given popular webinars on recommender systems and on ethical issues in social computing research, and has taught dozens of short courses and tutorials on recommender systems, human-computer interaction, and related topics. Dr. Konstan received his A.B. from Harvard (1987) and his M.S. (1990) and Ph.D. (1993) from the University of California, Berkeley, all in Computer Science. He has been elected a Fellow of the ACM, IEEE, and AAAS, and a member of the CHI Academy. He is also a past President of ACM SIGCHI, the 4500-member Special Interest Group on Human-Computer Interaction, a member of the ACM Council, and vice-Chair of ACM's Publications Board. He chaired the first ACM Conference on Recommender Systems in 2007, as well as other conferences including ACM UIST 2003 and ACM CHI 2012.

Reviews

4.3

172 total reviews

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By Benjamin S S on 13-Feb-19

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.

By Rashid K on 2-Jan-18

well one thing I am struggling with programming in JAVA. Would not it be handy to have option to do assignment using languages like python/R? which are basically language of choice for data scientists and also easy to have grasp on for newbies. one more thing some time I just get stuck and felt like now way out. I did not get any answer/help form posts on the forum .

By Nicols A on 28-Jun-18

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

By Mai H S on 20-Jan-19

good exercises & lectures

By Mustafa S on 8-Feb-19

Great course

By sidra n on 15-Aug-18

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 tao L on 22-Jul-18

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 sagar s on 4-Oct-18

Awesome. Worth it!

By on 5-Oct-16

, !

By Daniel P on 8-Dec-17

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 Sonia F on 6-Feb-17

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

By Shuang L on 21-Nov-17

great professors and inspiring lectures!

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