Worker recommendation for crowdsourced Q & A services: A Triple-Factor Aware approach

Worker Recommendation (WR) is one of the most impor- tant functions for crowdsourced Q & A services. Specifically, given a set of tasks to be solved, WR recommends each task with a certain group of workers, whom are expected to give timely answers with high qualities. To address the WR problem, recent studies have introduced a number of rec- ommendation approaches, which take advantage of workers' expertises or preferences towards different types of tasks. However, without a thorough consideration of workers' char- acters, such approaches will lead to either inadequate task fulfillment or inferior answer quality. In this work, we propose the Triple-factor Aware Worker Recommendation framework, which collectively considers workers' expertises, preferences and activenesses to maxi- mize the overall production of high quality answers. We con- struct the Latent Hierarchical Factorization Model, which is able to infer the tasks' underlying categories and workers' latent characters from the historical data; and we propose a novel parameter inference method, which only requires the processing of positive instances, giving rise to significantly higher time efficiency and better inference quality. What's more, the sampling-based recommendation algorithm is de- veloped, such that the near optimal worker recommendation can be generated for a presented batch of tasks with consid- erably reduced time consumption. Comprehensive experi- ments have been carried out using both real and synthetic datasets, whose results verify the effiectiveness and efficiency of our proposed methods.