In citizen science, regular people provide invaluable information by contributing to scientific projects. Citizen science platforms, such as SciStarter, provide easy access to numerous such projects. Often, users contribute mainly to a relatively small set of popular projects, while it is difficult for many projects to draw the attention of users. Thus, increasing the contribution of users to such low-popularity projects may increase scientific and societal impact. In this paper, we explore the power of a recommender system to draw attention to less popular projects. Standard use of recommendation systems often leads to limited exposure of less popular (tail) projects. We thus propose a re-ranking approach based on “lift boosting,” which uses the statistical lift measure to enhance the exposure of tail projects. By combining lift and traditional relevance measures, our method re-ranks the recommendation list to emphasize projects that are both relevant to the user while also have a high lift value. We implement our approach on SciStarter, one of the biggest citizen science platforms on the web. We conduct an online experiment involving over 2000 real users. Our results show a positive shift towards less popular projects without compromising overall contribution rates. This work demonstrates the potential of our lift-boosting method for promoting the discovery of tail projects in citizen science platforms, thereby fostering a more diverse range of scientific contributions.

Citizen science projects promise to increase scientific productivity while also connecting science with the general public. They create scientific value for researchers and provide pedagogical and social benefits to volunteers. Given the astounding number of available citizen science projects, volunteers find it difficult to find the projects that best fit their interests. This difficulty can be alleviated by providing personalized project recommendations to users. This paper studies whether combining project recommendations with explanations improves users’ contribution levels and satisfaction. We generate post-hoc explanations to users by learning from their past interactions as well as project content (e.g., location, topics). We provide an algorithm for clustering recommended projects to groups based on their predicted relevance to the user. We demonstrated the efficacy of our approach in offline studies as well as in an online study in SciStarter that included hundreds of users. The vast majority of users highly preferred receiving explanations about why projects were recommended to them, and receiving such explanations did not impede on the contribution levels of users, when compared to other users who received project recommendations without explanations. Our approach is now fully integrated in SciStarter.

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.