  Session-based recommendation aims to generate recommendations for the next
item of users' interest based on a given session. In this manuscript, we
develop prospective preference enhanced mixed attentive model (P2MAM) to
generate session-based recommendations using two important factors: temporal
patterns and estimates of users' prospective preferences. Unlike existing
methods, P2MAM models the temporal patterns using a light-weight while
effective position-sensitive attention mechanism. In P2MAM, we also leverage
the estimate of users' prospective preferences to signify important items, and
generate better recommendations. Our experimental results demonstrate that
P2MAM models significantly outperform the state-of-the-art methods in six
benchmark datasets, with an improvement as much as 19.2%. In addition, our
run-time performance comparison demonstrates that during testing, P2MAM models
are much more efficient than the best baseline method, with a significant
average speedup of 47.7 folds.
