Find it if you can: a game for modeling different types of web search success using interaction data

A better understanding of strategies and behavior of successful searchers is crucial for improving the experience of all searchers. However, research of search behavior has been struggling with the tension between the relatively small-scale, but controlled lab studies, and the large-scale log-based studies where the searcher intent and many other important factors have to be inferred. We present our solution for performing controlled, yet realistic, scalable, and reproducible studies of searcher behavior. We focus on difficult informational tasks, which tend to frustrate many users of the current web search technology. First, we propose a principled formalization of different types of ―success‖ for informational search, which encapsulate and sharpen previously proposed models. Second, we present a scalable game-like infrastructure for crowdsourcing search behavior studies, specifically targeted towards capturing and evaluating successful search strategies on informational tasks wi...

• Type: Journal
• Year: 2011
• Where: SIGIR
• Authors: Mikhail Ageev, Qi Guo, Dmitry Lagun, Eugene Agichtein
• Keywords: Information Technology, Reproducible Studies, SIGIR 2011, Success Models, Web Search Technology