adaptive crowd query processing
A Framework for Adaptive Crowd Query Processing
Trushkowsky, Beth (University of California, Berkeley) | Kraska, Tim (Brown University) | Franklin, Michael J. (University of California, Berkeley)
Search engines can yield poor results for information retrieval tasks when they cannot interpret query predicates. Such predicates are better left for humans to evaluate. We propose an adaptive processing framework for deciding (a) which parts of a query should be processed by machines and (b) the order the crowd should process the remaining parts, optimizing for result quality and processing cost. We describe an algorithm and experimental results for the first framework component.