  In this paper, we proposed a novel framework which uses user interests
inferred from activities (a.k.a., activity interests) in multiple social
collaborative platforms to predict users' platform activities. Included in the
framework are two prediction approaches: (i) direct platform activity
prediction, which predicts a user's activities in a platform using his or her
activity interests from the same platform (e.g., predict if a user answers a
given Stack Overflow question using the user's interests inferred from his or
her prior answer and favorite activities in Stack Overflow), and (ii)
cross-platform activity prediction, which predicts a user's activities in a
platform using his or her activity interests from another platform (e.g.,
predict if a user answers a given Stack Overflow question using the user's
interests inferred from his or her fork and watch activities in GitHub). To
evaluate our proposed method, we conduct prediction experiments on two widely
used social collaborative platforms in the software development community:
GitHub and Stack Overflow. Our experiments show that combining both direct and
cross-platform activity prediction approaches yield the best accuracies for
predicting user activities in GitHub (AUC=0.75) and Stack Overflow (AUC=0.89).
