  In this work, we study the problem of monotone non-submodular maximization
with partition matroid constraint. Although a generalization of this problem
has been studied in literature, our work focuses on leveraging properties of
partition matroid constraint to (1) propose algorithms with theoretical bound
and efficient query complexity; and (2) provide better analysis on theoretical
performance guarantee of some existing techniques. We further investigate those
algorithms' performance in two applications: Boosting Influence Spread and
Video Summarization. Experiments show our algorithms return comparative results
to the state-of-the-art algorithms while taking much fewer queries.
