Worker Recommendation (WR.) is one of the most important 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 recommendation approaches, which take advantage of workers' expertises or preferences towards different types of tasks. However, without a thorough consideration of workers' characters, 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 maximize the overall production of high quality answers. We construct 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 developed, such that the near optimal worker recommendation can be generated for a presented batch of tasks with considerably reduced time consumption. Comprehensive experiments have been carried out using both real and synthetic datasets, whose results verify the effectiveness and efficiency of our proposed methods.
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