  We investigate crowdsourcing algorithms for finding the top-quality item
within a large collection of objects with unknown intrinsic quality values.
This is an important problem with many relevant applications, for example in
networked recommendation systems. The core of the algorithms is that objects
are distributed to crowd workers, who return a noisy and biased evaluation. All
received evaluations are then combined, to identify the top-quality object. We
first present a simple probabilistic model for the system under investigation.
Then, we devise and study a class of efficient adaptive algorithms to assign in
an effective way objects to workers. We compare the performance of several
algorithms, which correspond to different choices of the design
parameters/metrics. In the simulations we show that some of the algorithms
achieve near optimal performance for a suitable setting of the system
parameters.
