Prof. Kobi Gal

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.