Belle Tseng - Universal Web Search Relevance
With the fast penetration of the Web throughout the world, the number of search users has increased dramatically from many geographic locations. Search engines are now facing the problem of providing search results to many countries. Machine Learned Ranking (MLR) approach has shown successes in web search. With the increasing demand to develop effective ranking functions for many countries (domains), we face a big bottleneck of insufficient training data to build a learned ranker for each domain. In my talk, I will present two approaches to resolve this problem. The first is a tree-based adaptation that takes a ranking function from one domain and tunes it with a small amount of training data from the target domain.
Krzysztof Gajos - Automatically Generating Personalized User Interfaces
User Interfaces delivered with today's software are usually created in a one-size-fits-all manner, making implicit assumptions about the needs, abilities, and preferences of the "average user" and about the characteristics of the "average device." I argue that personalized user interfaces, which are adapted to a person’s devices, tasks, preferences, and abilities, can improve user satisfaction and performance. In this talk, I focus on the portion of my research, which demonstrates how this approach benefits people with motor impairments.