Intelligent recommendations for citizen science

Na'ama Dayan, Kobi Gal, Avi Segal, Guy Shani, Darlene Cavalier

Research output: Contribution to journalConference articlepeer-review

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 significantly increased the number of citizen science projects and allowed volunteers to contribute to these projects in dramatically new ways. For example, SciStarter, our partners in the project, is an online portal that offers more than 3,000 affiliate projects and recruits volunteers through media and other organizations, bringing citizen science to people. 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. This paper addresses this challenge by developing a system for personalizing project recommendations in the SciStarter ecosystem. We adapted several recommendation algorithms from the literature based on collaborative filtering and matrix factorization. The algorithms were trained on historical data of users' interactions in SciStarter as well as their contributions to different projects. The trained algorithms were deployed in SciStarter in a study involving hundreds of users who were provided with personalized recommendations for projects they had not contributed to before. Volunteers were randomly divided into different cohorts, which varied the recommendation algorithm that was used to generate suggested projects. The results show that using the new recommendation system led people to contribute to new projects that they had never tried before and led to increased participation in SciStarter projects when compared to cohort groups that were recommended the most popular projects, or did not receive recommendations, In particular, the cohort 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 confirms that users were satisfied with the recommendation tool and claimed that the recommendations matched their personal interests and goals. Based on the positive results, our recommendation system is now fully integrated with SciStarter. The research has transformed how SciStarter helps projects recruit and support participants and better respond to their needs.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume2697
StatePublished - 2020
Event2020 Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems, ComplexRec-ImpactRS 2020 - Virtual, Online
Duration: Sep 25 2020 → …

ASJC Scopus subject areas

  • Computer Science(all)

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