CourseraIntroduction to Computer ScienceFree courseCertificate

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

4.3

86 reviews

Created by: Joseph A Konstan

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Overall Score : 86 / 100

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
86 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.

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Reviews

4.3

86 reviews in total

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By Nesreen S on 14-Nov-19

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 王嘉奕 on 6-Nov-19

Excellent course which helps me a lot.

By jonghee on 29-Oct-19

good lecture

By P S on 26-Sep-19

Nice course

By Muhammad Z H on 17-Sep-19

Learnt alot

By Alejo P on 13-Sep-19

The course is really well oriented, topics are broadly covered with good explanations and examples. One major drawback of this course is that the honors track is not implemented in Python, though I believe that possibly in future versions this will be adapted. In my case, the two options left are either I learn Java programming or I do not take the honors track.

By Julia K on 9-Sep-19

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 Lucas P B on 4-Sep-19

Was expecting programming activities in Python or R, not in Java =/

By Alex B on 26-Aug-19

This course mostly works. Contains a lot of wasted video time where no information is communicated. Uses simplistic tools that don't scale to data applications or otherwise dated tools not really used by data scientists or machine learning engineers making exercises either simplistic or a waste of time. Better than other courses in the series in that the assignments are legible.

By Su L on 23-Aug-19