Evaluating Recommender Systems
by Guy Shani
Guy Shani is a professor at the Information Systems Engineering department at the Ben-Gurion University, Israel. Previously, he was working at Microsoft Research, in the Machine Learning and Applied Statistics group. He is interested in recommender system applications, and has participated in a number of commercial implementations of such systems. He is also interested in automated decision making under uncertainty, and in the application of decision making techniques to recommender system problems.
HCI for Recommender Systems: An Introduction
by Joseph A. Konstan
This tutorial is a brief introduction to concepts and techniques from human-computer interaction, focused on designing usable interfaces. Topics covered include task analysis, user analysis, prototyping and design techniques, interface evaluation, and various processes for interface design. The tutorial is intended for those who do not have an HCI background (all of the content would be found in a typical undergraduate course on the topic), and it is focused on practical techniques that could be applied when designing recommender systems for end-users.
About the speaker
Joseph A. Konstan is Distinguished McKnight University Professor of Computer Science and Engineering at the University of Minnesota where he also holds the title Distinguished University Teaching Professor. He has worked in the field of human-computer interaction for more than 20 years, and in the field of recommender systems for more than 15. He co-directs the GroupLens Research project, co-founded Net Perceptions, Inc., and chaired the first ACM Recommender Systems conference in 2007. Much of Konstan’s work today involves research on human interaction issues in recommender systems, social computing more broadly, and persuasive computing for public health.
Query Intent Prediction and Recommendation
by Ricardo Baeza-Yates
User queries in search engines and Websites give valuable information on the interests of people. In addition, clicks after queries relate those interests to actual content. Even queries without clicks or answers imply important missing synonyms or content. In fact, queries are perhaps the best source of on-line wisdom of crowds in the Web. In this tutorial we show how we can use queries to improve the performance of search engines. In particular we focus on different techniques that have been proposed to predict the intent of a query and how that impacts the answer of it. We also cover one important application of this: query suggestions and query recommendations.
About the speaker
Ricardo Baeza-Yates is VP of Yahoo! Research for Europe, Middle East and Latin America, leading the labs at Barcelona, Spain and Santiago, Chile, as well as supervising the newer lab in Haifa, Israel. He is co-author of the best-seller book Modern Information Retrieval, published in 1999 by Addison-Wesley with a second edition coming in 2010. He has received the Organization of American States award for young researchers in exact sciences of 1993 and he was the first computer scientist to be elected to the Chilean Academy of Sciences in 2003. During 2007 he was awarded the Graham Medal for innovation in computing, given by the University of Waterloo to distinguished ex-alumni. In 2009 he was awarded the Latin American distinction for contributions to CS in the region and became an ACM Fellow.
