  Movie recommendation systems provide users with ranked lists of movies based
on individual's preferences and constraints. Two types of models are commonly
used to generate ranking results: long-term models and session-based models.
While long-term models represent the interactions between users and movies that
are supposed to change slowly across time, session-based models encode the
information of users' interests and changing dynamics of movies' attributes in
short terms. In this paper, we propose an LSIC model, leveraging Long and
Short-term Information in Content-aware movie recommendation using adversarial
training. In the adversarial process, we train a generator as an agent of
reinforcement learning which recommends the next movie to a user sequentially.
We also train a discriminator which attempts to distinguish the generated list
of movies from the real records. The poster information of movies is integrated
to further improve the performance of movie recommendation, which is
specifically essential when few ratings are available. The experiments
demonstrate that the proposed model has robust superiority over competitors and
sets the state-of-the-art. We will release the source code of this work after
publication.
