Nidhi Hegde, Nokia Bell Labs, France


Learning to match in expert systems: experiments on stackexchange data


We study adaptive matching in expert systems. Consider a system that receives tasks or jobs to be classified into one of a set of given types. The system has access to a set of workers, or experts, and the expertise of a worker is defined by the jobs he is able to classify and the error in his response. This active sequential hypothesis testing problem was first addressed by Chernoff in 1959. In our work we assume access to less fine-grained information about the expertise of workers. We propose an algorithm, show its optimality and through numerical results show that it outperforms the Chernoff algorithm. Our work can be applied to various applications such as crowdsourcing systems and Q&A forums like StackExchange.