Research on Recommendation Algorithm for Mobile Application Crowdsourcing Testers

DOI

10.2991/mse-17.2017.64How to use a DOI?

Keywords

Mobile application crowdsourcing testing; Top-K algorithm; tester recommendation; matching degree

Abstract

The anonymous crowdsourcing testers determine the quality of tests, and the low matching degree between testers and tasks reduce testers' enthusiasm. To match the recommended testers with tasks, a two phase recommendation method based on Top-K algorithm was proposed. Category was introduced to reduce time complexity of Top-K algorithm. By classifying the tasks and calculating the category matching scores, the testers were most suitable for the category of the task were obtained. After calculating the similarity between tester portrayal and tasks, the top K testers were recommended from selected categories. Experiment shows that the proposed Top-K-Worker algorithm can greatly improve the matching degree between testers and the recommended task.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ying Liu
AU  - Tao Zhang
AU  - Kun Li
PY  - 2017/10
DA  - 2017/10
TI  - Research on Recommendation Algorithm for Mobile Application Crowdsourcing Testers
BT  - Proceedings of the 3rd Annual 2017 International Conference on Management Science and Engineering (MSE 2017)
PB  - Atlantis Press
SP  - 275
EP  - 279
SN  - 2352-5428
UR  - https://doi.org/10.2991/mse-17.2017.64
DO  - 10.2991/mse-17.2017.64
ID  - Liu2017/10
ER  -