Sequential crowdsourcing and recommendation strategies - Kang, Qiyu (Nanyang Technological University, 2020)
Crowdsourcing has emerged as an online, distributed problem-solving and production model where many participating workers are involved in performing tasks collaboratively in domains such as gathering language-related data, labeling the content of an image, or voting. In modern online platforms, including crowdsourcing, recommendation systems play an essential role and have become indispensable. To provide better service in crowdsourcing and recommendation, in this thesis, we study sequential crowdsourcing and recommendation strategies where historical data will be explored when making decisions at the current time step.