Name
Abstract | ||
|---|---|---|
We present InCognitoMatch, the first cognitive-aware crowdsourcing application for matching tasks. InCognitoMatch provides a handy tool to validate, annotate, and correct correspondences using the crowd whilst accounting for human matching biases. In addition, InCognitoMatch enables system administrators to control context information visible for workers and analyze their performance accordingly. For crowd workers, InCognitoMatch is an easy-to-use application that may be accessed from multiple crowdsourcing platforms. In addition, workers completing a task are offered suggestions for followup sessions according to their performance in the current session. For this demo, the audience will be able to experience InCognitoMatch thorough three use-cases, interacting with system as workers and as administrators.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3318464.3384697 | SIGMOD/PODS '20: International Conference on Management of Data
Portland
OR
USA
June, 2020 |
Keywords | DocType | ISBN |
Data Integration, Crowdsourcing, Matching, Cognitive-aware | Conference | 978-1-4503-6735-6 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roee Shraga | 1 | 11 | 7.27 |
| Coral Scharf | 2 | 1 | 0.35 |
| Rakefet Ackerman | 3 | 34 | 3.96 |
| Avigdor Gal | 4 | 23 | 2.45 |