Many widely used Recommender System algorithms estimate user tastes without accounting for their evolving nature. In recent years there has been a gradual increase in methods incorporating such temporal dynamics through sequential processing of user consumption histories. Some works have also included additional temporal features such as time stamps and intervals between a given user’s interactions with the platform. The latter, in particular, may be a strong signal providing additional context with respect to the current user preferences. However, in previous works this source of information has only been used passingly, without any significant analysis of its impact on recommendation. In this thesis we examine the effects of such intervals, termed time gaps, on recommendation accuracy. In order to do so, we propose a family of novel DeepTimeDelta models, extending a state-of-the-art sequential Recurrent Neural Network based recommender. Through the comparison of our time-dependent models to the sequential baseline we demonstrate that the use of time gaps leads to improvements in recommendation performance, in particular for cases following longer user inactivity. Furthermore, we examine the mechanisms regulating the model recommendation behaviour. Our results suggest that the above performance improvements may be achieved through increased reliance on user long term preferences as well as strong regulation of the importance of the recently consumed items. Finally, we examine the performance differences for users groups with distinct consumption behaviours, demonstrating some improvement for groups featuring less active users as well as users consuming more popular content.