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

Wisdom in Sum of Parts: Multi-Platform Activity Prediction in Social Collaborative Sites