IGATA: An Attraction-Based Online Task Recommendation Framework in Freemium-Crowdsourcing Platform

The Freemium-Crowdsourcing platform replaces purchases in Freemium games with crowdsourcing tasks, which can effectively incentivize player participation using the psychological factor of reward attraction. However, no previous researches focus on quantifying this factor, nor do they make an effort on controlling the answer quality produced by the hybrid platform. We propose a novel online algorithm, IGATA, to allocate crowdsourcing tasks to Freemium game players on the hybrid Freemium-Crowdsourcing platform. In order to maximize the task satisfaction to reduce waste caused by task refusal, while keeping competitive expected profit output, we measure the players' quality using their in-game attributes, and quantify the task reward attraction as the key factor of allocation with the help of psychological knowledge. Evaluations conducted on real trace data show that IGATA makes a significant improvement in producing total attraction comparing to existing strategies.