Platform for Task Recommendation in Human Computation

Crowdsourcing and Human computation have enabled industry andscientists to create innovative solutions by harnessing organisedcollective human effort. In human computation platforms, it isobserved that workers spend large amount of time searching forappropriate tasks due to lack of effective task discovery mechanism. This loss of time translates to loss of incentives for workerand affects motivation to solve more tasks. Task recommendationin human computation can not only help mitigating this problem,but it can also result in high quality answers from motivated workers. While few works empirically proved the benefits of task recommendation in human computation platforms, we advocate for abetter scientific understanding of how worker- and task-modellingcan help to systematically achieve faster and higher-quality task executions. To this end, it is fundamental the availability of tools ableto “open the box” of human computation, and offer direct controlon worker and task properties, to be later used for recommendation. This paper presents BruteForce, a framework that simplifies experiments with commercial human computation platforms,while offering task recommendation features based on rich (andextensible) set of worker and task properties. We describe the characteristics of BruteForce, and we report on a set of preliminaryexperiments with three user profiling techniques namely featureindependent, feature-based and Composite.Multimedia ComputingWeb Information System