1,525 research outputs found
Finish Them!: Pricing Algorithms for Human Computation
Given a batch of human computation tasks, a commonly ignored aspect is how
the price (i.e., the reward paid to human workers) of these tasks must be set
or varied in order to meet latency or cost constraints. Often, the price is set
up-front and not modified, leading to either a much higher monetary cost than
needed (if the price is set too high), or to a much larger latency than
expected (if the price is set too low). Leveraging a pricing model from prior
work, we develop algorithms to optimally set and then vary price over time in
order to meet a (a) user-specified deadline while minimizing total monetary
cost (b) user-specified monetary budget constraint while minimizing total
elapsed time. We leverage techniques from decision theory (specifically, Markov
Decision Processes) for both these problems, and demonstrate that our
techniques lead to upto 30\% reduction in cost over schemes proposed in prior
work. Furthermore, we develop techniques to speed-up the computation, enabling
users to leverage the price setting algorithms on-the-fly
Optimization in Knowledge-Intensive Crowdsourcing
We present SmartCrowd, a framework for optimizing collaborative
knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by
accounting for human factors in the process of assigning tasks to workers.
Human factors designate workers' expertise in different skills, their expected
minimum wage, and their availability. In SmartCrowd, we formulate task
assignment as an optimization problem, and rely on pre-indexing workers and
maintaining the indexes adaptively, in such a way that the task assignment
process gets optimized both qualitatively, and computation time-wise. We
present rigorous theoretical analyses of the optimization problem and propose
optimal and approximation algorithms. We finally perform extensive performance
and quality experiments using real and synthetic data to demonstrate that
adaptive indexing in SmartCrowd is necessary to achieve efficient high quality
task assignment.Comment: 12 page
Personalized human computation
Significant effort in machine learning and information retrieval has been devoted to identifying personalized content such as recommendations and search results.
Personalized human computation has the potential to go beyond existing techniques like collaborative filtering to provide personalized results on demand, over personal data, and for complex tasks. This work-in-progress compares two approaches to personalized human computation. In both, users annotate a small set of training examples which are then used by the crowd to annotate unseen items. In the first approach, which we call taste-matching, crowd members are asked to annotate the same set of training examples, and the ratings of similar users on other items are then used to infer personalized ratings. In the second approach, taste-grokking, the crowd is presented with the training examples and asked to use them predict the ratings of the target user on other items
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