  This analysis explores the temporal sequencing of objects in a movie trailer.
Temporal sequencing of objects in a movie trailer (e.g., a long shot of an
object vs intermittent short shots) can convey information about the type of
movie, plot of the movie, role of the main characters, and the filmmakers
cinematographic choices. When combined with historical customer data,
sequencing analysis can be used to improve predictions of customer behavior.
E.g., a customer buys tickets to a new movie and maybe the customer has seen
movies in the past that contained similar sequences. To explore object
sequencing in movie trailers, we propose a video convolutional network to
capture actions and scenes that are predictive of customers' preferences. The
model learns the specific nature of sequences for different types of objects
(e.g., cars vs faces), and the role of sequences in predicting customer future
behavior. We show how such a temporal-aware model outperforms simple feature
pooling methods proposed in our previous works and, importantly, demonstrate
the additional model explain-ability allowed by such a model.

Computer Vision and Pattern Recognition;Artificial Intelligence;Information Retrieval;Machine Learning