  As more and more people shift their movie watching online, competition
between movie viewing websites are getting more and more intense. Therefore, it
has become incredibly important to accurately predict a given user's watching
list to maximize the chances of keeping the user on the platform. Recent
studies have suggested that the novelty-seeking propensity of users can impact
their viewing behavior. In this paper, we aim to accurately model and describe
this novelty-seeking trait across many users and timestamps driven by data,
taking into consideration user forgetfulness. Compared to previous studies, we
propose a more robust measure for novelty. Our model, termed Deep Forgetful
Novelty-Seeking Model (DFNSM), leverages demographic information about users,
genre information about movies, and novelty-seeking traits to predict the most
likely next actions of a user. To evaluate the performance of our model, we
conducted extensive experiments on a large movie rating dataset. The results
reveal that DFNSM is very effective for movie recommendation.
