  Session-based Recurrent Neural Networks (RNNs) are gaining increasing
popularity for recommendation task, due to the high autocorrelation of user's
behavior on the latest session and the effectiveness of RNN to capture the
sequence order information. However, most existing session-based RNN
recommender systems still solely focus on the short-term interactions within a
single session and completely discard all the other long-term data across
different sessions. While traditional Collaborative Filtering (CF) methods have
many advanced research works on exploring long-term dependency, which show
great value to be explored and exploited in deep learning models. Therefore, in
this paper, we propose ASARS, a novel framework that effectively imports the
temporal dynamics methodology in CF into session-based RNN system in DL, such
that the temporal info can act as scalable weights by a parallel attentional
network. Specifically, we first conduct an extensive data analysis to show the
distribution and importance of such temporal interactions data both within
sessions and across sessions. And then, our ASARS framework promotes two novel
models: (1) an inter-session temporal dynamic model that captures the long-term
user interaction for RNN recommender system. We integrate the time changes in
session RNN and add user preferences as model drifting; and (2) a novel
triangle parallel attention network that enhances the original RNN model by
incorporating time information. Such triangle parallel network is also
specially designed for realizing data argumentation in sequence-to-scalar RNN
architecture, and thus it can be trained very efficiently. Our extensive
experiments on four real datasets from different domains demonstrate the
effectiveness and large improvement of ASARS for personalized recommendation.
