Name
Abstract | ||
|---|---|---|
Knowledge-intensive crowdsourcing (KI-C) plays an important role in today’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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-55393-7_32 | KSEM (2) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zizhe Wang | 1 | 5 | 1.76 |
| Hailong Sun | 2 | 680 | 64.83 |
| Tao Han | 3 | 11 | 1.55 |