Microtask crowdsourcing, as an efficient and economical method for a requester to outsource tasks to online workers, is becoming increasingly popular in many domains, especially collecting labels for large-scale datasets. In microtask crowdsourcing, a requester usually needs to accomplish three steps: firstly, recruit as many as possible workers from the market; then, assign tasks to the workers based on their performance; lastly, reward good workers and meanwhile punish bad workers. For these three steps, various mechanisms have been proposed. Under certain assumptions about workers’ responses to the rewards, these mechanisms can theoretically ensure workers to follow the strategies desired by the requester and thus maximize the revenue of the requester. However, these assumptions may be violated in practice, which causes the failure of these theoretically elegant mechanisms. Thereby, recent studies move their focus to the learning-based mechanisms which learn workers’ models in an online fashion rather than simply assumin g one. In this thesis, we propose three novel learning-based mechanisms, each for one step, to push forward the studies in this direction.
December 10th, 2018
13:30 ~ 15:00
Zehong Hu,Ph.D. degree from Nanyang Technological University (NTU)
Room 308, School of Information Management & Engineering, Shanghai University of Finance & Economics