Recommender Systems: The Power of Personalization with Joseph Konstan
Personalization is the key to helping guide users through the morass of available choices to the products and information they seek. Industry leaders such as Amazon.com, Microsoft, Google, and E-Bay have long used recommender systems to improve their offerings and better serve their customers. But recommender systems aren't limited to big technology firms—they've been widely used by small information providers, retailers, and service firms.
This webinar provides an introduction to recommender systems, describing the different types of recommendation technologies available and how they are used in different applications today.
What You'll Learn About:
- What are recommender systems and how are they used today?
- The different types of recommender systems: -- content-based vs. collaborative recommendation -- ephemeral vs. persistent personalization
- User profiles, site logs, and the information used in recommendation
- An introduction to the basic technology of recommendation
- Pointers to resources for further learning
Joseph is Distinguished McKnight University Professor and Distinguished University Teaching Professor of Computer Science and Engineering at the University of Minnesota. He has been working in the field of recommender systems since 1995. He's published more than fifty research articles on the topic, holds five patents related to recommender systems, and co-authored the book Word of Mouse: The Marketing Power of Collaborative Filtering, one of the first books on the application of recommender systems to commercial systems. Konstan chaired the first ACM Conference on Recommender Systems, and has been active in ACM, including serving as President of ACM SIGCHI from 2003-2006; he is now starting his third term on the ACM Council. He co-founded Net Perceptions, Inc. in 1996. The company commercialized recommendation engines and had a variety of online and bricks-and-mortar companies among its customers, including Amazon.com. He is a Fellow of the ACM, has been elected to the SIGCHI Academy, and was part of the team that won the 2010 ACM Software Systems Award for the GroupLens Collaborative Filtering Recommender Systems.