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III: Small: A Big Data and Machine Learning Approach for Improving the Efficiency of Two-sided Online Labor Markets

Sponsored by National Science Foundation

$600K Funding
1 People

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Online labor markets (OLMs), where the employment of on-demand workers and the delivery of products occur online, have become an increasingly important component of today?s economy. These two-sided markets provide workers and employers with the access to a large pool of geographically dispersed, semi-anonymous virtual transaction partners. Millions of Americans are freelancing at various OML platforms, and they contribute hundreds of billions of dollars to the U.S. economy. To support workers and employers in performing daily business, OLM platforms have developed some functions and tools. Examples include keyword-based search for jobs, task matching, and recommendation for workers. However, many of these tools have critical drawbacks and cause significant problems (such as bias in the exposure of tasks to workers). This project will collect and analyze massive OLM data and develop a suite of novel technical solutions to improve the efficiency of today?s OLM platforms. The developed technical approaches will make research contributions to the fields of data mining, machine learning, and OLM analytics. The study results of this project will advance the knowledge and understanding of the U.S. OLMs. This project is designed to benefit multiple stakeholders and yield potential impacts on society such as providing a novel worker-task matching tool for OLM platforms, updating OLM workers? skills, and improving OLM employment. This project integrates research with education through course module development, involving graduate and undergraduate students in research, and research showcases for local K-12 students. This project focuses on the following three specific aims (SAs): developing novel technical solutions for discovering useful knowledge, learning feature representations from massive OLM data, studying the issues of over-competition among workers, mitigating unbalanced exposure of tasks in OLMs, developing a novel two-sided matching approach to address the issues, analyzing the critical skill gap between OLM tasks and workers, and developing a novel forward-looking OLM skill recommendation solution to bridge the gap. The project will develop a context-aware deep & wide approach for mining OLM skill keywords from massive text and a new representation-learning approach that jointly models graphical and textual data. It will formalize and solve an optimization problem for two-sided matching between tasks and workers. It will also develop an effective approach for identifying OLM workers who need skill updating and an interpretable heuristic approach for generating skill recommendations to selected workers. The results of this project will be disseminated in the form of peer-reviewed publications, conference presentations, seminars, and workshops. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.