IEEE MLSP 2023 Data Challenge - Volunteer Retention and Future Collaboration Prediction


In recent years, there has been a rise in online crowdsourcing platforms that facilitate volunteering efforts, enabling individuals to collaborate on collective goals. Such efforts can play an instrumental role in responding to community needs, especially during times of crisis, such as earthquakes and pandemics. To optimize these efforts, it is crucial to model volunteers’ task participation and collaboration behavior. In this challenge, we aim to learn such a model from data using machine learning and signal processing techniques. We present a volunteer participation dataset with two tasks.

Volunteer Dataset

The dataset is collected from a mobile crowdsourcing app called “Anti-Pandemic Pioneers” (later renamed to “Shenzhen Pioneers”) used for organizing volunteers during the COVID-19 pandemics in Shenzhen, China. It contains records of each volunteer participating in a group activity (task). Each record includes features such as volunteer ID, task ID, timestamp, task location, and etc. Task names and descriptions will also be provided.


Heapmap of volunteer activities from 2020 to 2021 in Shenzhen

Task 1: Retention Prediction

Predict the number of future participations of volunteers based on their participation history. Groundtruth labels will be provided as a list of “Volunteer ID - # of future participation” pairs. This task focuses on modeling volunteer retention, an important research area in social and management science. Improving volunteer retention enhances the overall volunteering experience and encourages sustained engagement.

Task 2: Future Collaboration Prediction

Predict whether two volunteers will collaborate in future tasks. The particiaption records of volunteers as well as the ground truth edge list of their participation graph will be provided. Future collaboration prediction has diverse applications, such as understanding volunteer group behaviors and developing social recommendation tools for volunteers.

Competition Details

See Task Description for detailed information about the dataset and evaluation criteria.

Challenge Winner and Paper Selection Rules

Code Submission

Submissions can be made for the public test on Kaggle (add a link), and immediate scores (MSE and AUPR) will be provided.

Paper Submission

Final Paper Submission


A subset of this dataset was first published with the following paper:

Zhang, A., Zhang, K., Li, W., Wang, Y., Li, Y. and Zhang, L., 2022. Optimising Self-organised volunteer efforts in response to the COVID-19 pandemic. Humanities and Social Sciences Communications, 9(1).

Important Date:

  • July 19, 2023: Kaggle Code Submission Deadline

  • July 20, 2023: Paper Summary Submission Deadline

  • July 27, 2023: Final Paper Submission Deadline

  • July 29, 2023: Notification of Paper Acceptance

  • July 31, 2023: Camera Ready Submission

Contact Us:

Challenge Organizers: