Users may show a behavioral pattern in consuming the items. For example, one might assume that a user is interested in comedy movies when this user watches comedy movies frequently. Recommender systems are designed to understand the preference of a user from his interactions with the items and suggest items that correspond to his preference. Therefore, observing users’ behavioral pattern in consuming items is essential to capture users’ interest. We define this behavioral pattern of the users in consuming the items as a user dynamic. In this thesis, we investigate user dynamic patterns/features that can be extracted from the implicit feedback and further utilize such features to further improve the recommender systems. Moreover, we aim to leverage such user dynamic features to improve the recommender systems to predict the preferred items in the far future. Three types of item features are explored to discover potential user dynamic patterns: we explore whether a user tends to consume an item that has been rated frequently by the similar users; we explore the moment when an item was rated may influence the probability that this item is rated by a user in the future; and we explore whether the popularity of an item may influence the probability that this item is rated by a user in the future. Some useful patterns are observed from the user dynamics analysis. Furthermore, we propose several
modeling approaches to integrate the observed user dynamic features into the recommender systems, and we start to integrate it into the Bayesian Personalized Ranking Matrix Factorization (BPR-MF). The results show that modeling user dynamics could improve the prediction performance of BPR-MF.