@inproceedings{889b214723914993be47fbf23b43c862,
title = "Predicting Crowdsourcing Worker Performance with Knowledge Tracing",
abstract = "Knowledge-intensive crowdsourcing (KI-C) plays an important role in today{\textquoteright}s knowledge economy. And competitive knowledge-intensive crowdsourcing (CKI-C) is a kind of KI-C in which tasks are released in the form of competitions. The worker performance prediction is important for CKI-C platforms to recommend tasks to proper workers. Traditional worker performance prediction methods do not consider the complex properties of tasks and worker skills, thus they do not function in CKI-C. In this work, we design the KT4Crowd framework to incorporate knowledge tracing, used effectively in intelligent tutoring systems (ITS), into CKI-C for predicting worker performance. The experimental results on a large-scale Topcoder dataset show the effectiveness of our framework and the DKVMN model with our framework achieves the best performance among the compared state-of-the-art methods.",
keywords = "Crowdsourcing, Knowledge tracing, Performance prediction",
author = "Zizhe Wang and Hailong Sun and Tao Han",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 13th International Conference on Knowledge Science, Engineering and Management, KSEM 2020 ; Conference date: 28-08-2020 Through 30-08-2020",
year = "2020",
doi = "10.1007/978-3-030-55393-7\_32",
language = "英语",
isbn = "9783030553920",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "352--359",
editor = "Gang Li and Shen, \{Heng Tao\} and Ye Yuan and Xiaoyang Wang and Huawen Liu and Xiang Zhao",
booktitle = "Knowledge Science, Engineering and Management - 13th International Conference, KSEM 2020, Proceedings",
address = "德国",
}