Joseph A. Konstan

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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Evaluating User Interfaces (Coursera)

In this course you will learn and practice several techniques for user interface evaluation. First we start with techniques that can be applied alone or in a design team, including action analysis, walkthroughs, and heuristic evaluation. Then we move on to user testing, including learning from a [...]
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Recommender Systems: Evaluation and Metrics (Coursera)

In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to [...]
Average: 4 ( 3 votes )

Introduction to UI Design (Coursera)

In this course, you will gain an understanding of the critical importance of user interface design. You will also learn industry-standard methods for how to approach the design of a user interface and key theories and frameworks that underlie the design of most interfaces you use today. Through a [...]
Average: 3 ( 4 votes )

Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera)

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. [...]
Average: 6 ( 4 votes )

Matrix Factorization and Advanced Techniques (Coursera)

In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. [...]
Average: 6 ( 4 votes )

Prototyping and Design (Coursera)

In this course you will learn how to design and prototype user interfaces to address the users and tasks identified in user research. Through a series of lectures and exercises, you will learn and practice paper- and other low-fidelity prototyping techniques; you will learn and apply principles from [...]
Average: 10 ( 3 votes )

Nearest Neighbor Collaborative Filtering (Coursera)

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. [...]
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