  The aim of session-based recommendation is to predict the users' next clicked
item, which is a challenging task due to the inherent uncertainty in user
behaviors and anonymous implicit feedback information. A powerful session-based
recommender can typically explore the users' evolving interests (i.e., a
combination of his/her long-term and short-term interests). Recent advances in
attention mechanisms have led to state-of-the-art methods for solving this
task. However, there are two main drawbacks. First, most of the attention-based
methods only simply utilize the last clicked item to represent the user's
short-term interest ignoring the temporal information and behavior context,
which may fail to capture the recent preference of users comprehensively.
Second, current studies typically think long-term and short-term interests as
equally important, but the importance of them should be user-specific.
Therefore, we propose a novel Parallel Attention Network model (PAN) for
Session-based Recommendation. Specifically, we propose a novel time-aware
attention mechanism to learn user's short-term interest by taking into account
the contextual information and temporal signals simultaneously. Besides, we
introduce a gated fusion method that adaptively integrates the user's long-term
and short-term preferences to generate the hybrid interest representation.
Experiments on the three real-world datasets show that PAN achieves obvious
improvements than the state-of-the-art methods.
