  In the era of big data, logistic planning can be made data-driven to take
advantage of accumulated knowledge in the past. While in the movie industry,
movie planning can also exploit the existing online movie knowledge library to
achieve better results. However, it is ineffective to solely rely on
conventional heuristics for movie planning, due to a large number of existing
movies and various real-world factors that contribute to the success of each
movie, such as the movie genre, available budget, production team (involving
actor, actress, director, and writer), etc. In this paper, we study a
"Blockbuster Planning" (BP) problem to learn from previous movies and plan for
low budget yet high return new movies in a totally data-driven fashion. After a
thorough investigation of an online movie knowledge library, a novel movie
planning framework "Blockbuster Planning with Maximized Movie Configuration
Acquaintance" (BigMovie) is introduced in this paper. From the investment
perspective, BigMovie maximizes the estimated gross of the planned movies with
a given budget. It is able to accurately estimate the movie gross with a 0.26
mean absolute percentage error (and 0.16 for budget). Meanwhile, from the
production team's perspective, BigMovie is able to formulate an optimized team
with people/movie genres that team members are acquainted with. Historical
collaboration records are utilized to estimate acquaintance scores of movie
configuration factors via an acquaintance tensor. We formulate the BP problem
as a non-linear binary programming problem and prove its NP-hardness. To solve
it in polynomial time, BigMovie relaxes the hard binary constraints and
addresses the BP problem as a cubic programming problem. Extensive experiments
conducted on IMDB movie database demonstrate the capability of BigMovie for an
effective data-driven blockbuster planning.
