The bloodhound project: automating discovery of web usability issues using the InfoScentπ simulator
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Depth- and breadth-first processing of search result lists
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Are people biased in their use of search engines?
Communications of the ACM - Alternate reality gaming
Modeling information navigation: implications for information architecture
Human-Computer Interaction
Modeling user behavior using a search-engine
Proceedings of the 12th international conference on Intelligent user interfaces
Analysis of online video search and sharing
Proceedings of the eighteenth conference on Hypertext and hypermedia
Modeling user variance in time-biased gain
Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval
| Hi-index | 0.00 |
Search engine results are usually presented in some form of text summary (e.g., document title, some snippets of the page's content, a URL, etc). Based on the information contained within these summaries users make relevance judgments about what links best suit their information needs. Current research suggests that these relevance judgments are in the service of some search strategy. In this paper, we model two different search strategies (the comparison and threshold strategies) and determine how well they fit data gathered from an experiment on user search within a simulated Google environment.