Paper Info
Title
Find it if you can: a game for modeling different types of web search success using interaction data
Abstract
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 with known intent. Third, we report our analysis of search success using these data, which confirm and extends previous findings. Finally, we demonstrate that our model can predict search success more effectively than the existing state-of-the-art methods, on both our data and on a different set of log data collected from regular search engine sessions. Together, our search success models, the data collection infrastructure, and the associated behavior analysis techniques, significantly advance the study of success in web search.
Year
DOI
Venue
2011
10.1145/2009916.2009965
SIGIR
Keywords
Field
DocType
web search success,different type,associated behavior analysis technique,successful search strategy,regular search engine session,crowdsourcing search behavior study,informational search,search success model,search success,current web search technology,search behavior,web search,interaction data,behavior analysis,search engine,data collection,difference set
Data mining,Web search query,Data collection,World Wide Web,Search engine,Semantic search,Information retrieval,Computer science,Crowdsourcing,Search analytics,User studies,Scalability
Conference
Citations 
PageRank 
References 
85
2.13
27
Authors
4
Name
Order
Citations
PageRank
Mikhail S. Ageev11295.05
Qi Guo271634.09
Dmitry Lagun335114.96
Eugene Agichtein44549269.70