Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved ...
Current web search engines focus on searching only the most recent snapshot of the web. In some cases, however, it would be desirable to search over collections that include many ...
The University of Pittsburgh's Computing Services and Systems Development organization is focused on the needs of the faculty and student population, totaling nearly 40,000 u...
This paper describes a hidden Markov model (HMM) based approach to perform search interface segmentation. Automatic processing of an interface is a must to access the invisible co...
Finding topic experts on microblogging sites with millions of users, such as Twitter, is a hard and challenging problem. In this paper, we propose and investigate a new methodolog...