<p>Crowdsourcing initiatives design has been a heated topic as the applications&nbsp;of crowdsourcing on solving modularized problems for companies&nbsp;becomes more and more popular. However, the study of crowdsourcing&nbsp;design has been mostly focused on the theoretical&nbsp;side, using mathematical&nbsp;tools like game theory to address crowdsourcing design problems. This project&nbsp;offers a new perspective from the experience of real-life&nbsp;applications.&nbsp;An empirical dataset scraped from two major crowdsourcing platforms is&nbsp;constructed and relevant factors that are key&nbsp;determinants&nbsp;to the success of crowdsourcing&nbsp;initiative are extracted. The derived dataset is statistically analyzed&nbsp;with&nbsp;machine learning algorithms. After finding the dataset may be too small to&nbsp;be capable to train a prediction model that can fully embody&nbsp;the relationships hidden&nbsp;in the dataset, we&nbsp;incorporate existing&nbsp;theories&nbsp;from literature&nbsp;in the loop to generate synthetic&nbsp;dataset. Along the way, theoretical findings about crowdsourcing initiative design are examined with our empirical dataset to help researchers reflect on their work. Besides verifying existing theoretical&nbsp;knowledge about crowdsourcing design along building&nbsp;synthetic dataset,&nbsp;we are able to construct a recommender system with the dataset combining the empirical dataset and the synthetic dataset&nbsp;to give specific recommendations about crowdsourcing initiative designs&nbsp;once given the problem description part of factors and the expected outcome.</p>

Data-Driven Recommender System for Crowdsourcing Initiatives Design

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

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