Intelligent Recommendations for Citizen Science

Authors

  • Daniel Ben Zaken Ben-Gurion University
  • Kobi Gal Ben-Gurion University University of Edinburgh
  • Guy Shani Ben-Gurion University
  • Avi Segal Ben-Gurion University
  • Darlene Cavalier Arizona State University

Keywords:

Web, Environmental Sustainability, Education

Abstract

Citizen science refers to scientific research that is carried out by volunteers, often in collaboration with professional scientists. The spread of the internet has allowed volunteers to contribute to citizen science projects in dramatically new ways while creating scientific value and gaining pedagogical and social benefits. Given the sheer size of available projects, finding the right project, which best suits the user preferences and capabilities, has become a major challenge and is essential for keeping volunteers motivated and active contributors. We address this challenge by developing a system for personalizing project recommendations which was fully deployed in the wild. We adapted several recommendation algorithms to the citizen science domain from the literature based on memory-based and model-based collaborative filtering approaches. The algorithms were trained on historical data of users' interactions in the SciStarter platform - a leading citizen science site -as well as their contributions to different projects. The trained algorithms were evaluated in SciStarter and involved hundreds of users who were provided with personalized recommendations for new projects they had not contributed to before. The results show that using the new recommendation system led people to increased participation in new SciStarter projects when compared to groups that were recommended projects using non-personalized recommendation approaches, and compared to behavior before recommendations. In particular, the group of volunteers receiving recommendations created by an SVD algorithm (matrix factorization) exhibited the highest levels of contributions to new projects, when compared to the other cohorts. A follow-up survey conducted with the SciStarter community confirmed that users felt that the recommendations matched their personal interests and goals. Based on these results, our recommendation system is now fully integrated into the SciStarter portal, positively affecting hundreds of users each week, and leading to social and educational benefits.

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Published

2021-05-18

How to Cite

Ben Zaken, D., Gal, K., Shani, G., Segal, A., & Cavalier, D. (2021). Intelligent Recommendations for Citizen Science. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14693-14701. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17726

Issue

Section

AAAI Special Track on AI for Social Impact