  Modern social platforms are characterized by the presence of rich
user-behavior data associated with the publication, sharing and consumption of
textual content. Users interact with content and with each other in a complex
and dynamic social environment while simultaneously evolving over time. In
order to effectively characterize users and predict their future behavior in
such a setting, it is necessary to overcome several challenges. Content
heterogeneity and temporal inconsistency of behavior data result in severe
sparsity at the user level. In this paper, we propose a novel
mutual-enhancement framework to simultaneously partition and learn latent
activity profiles of users. We propose a flexible user partitioning approach to
effectively discover rare behaviors and tackle user-level sparsity. We
extensively evaluate the proposed framework on massive datasets from real-world
platforms including Q&A networks and interactive online courses (MOOCs). Our
results indicate significant gains over state-of-the-art behavior models ( 15%
avg ) in a varied range of tasks and our gains are further magnified for users
with limited interaction data. The proposed algorithms are amenable to
parallelization, scale linearly in the size of datasets, and provide
flexibility to model diverse facets of user behavior.
