  Session-based Recommendation (SR) aims to predict users' next click based on
their behavior within a short period, which is crucial for online platforms.
However, most existing SR methods somewhat ignore the fact that user preference
is not necessarily strongly related to the order of interactions. Moreover,
they ignore the differences in importance between different samples, which
limits the model-fitting performance. To tackle these issues, we put forward
the method, Mining Interest Trends and Adaptively Assigning Sample Weight,
abbreviated as MTAW. Specifically, we model users' instant interest based on
their present behavior and all their previous behaviors. Meanwhile, we
discriminatively integrate instant interests to capture the changing trend of
user interest to make more personalized recommendations. Furthermore, we devise
a novel loss function that dynamically weights the samples according to their
prediction difficulty in the current epoch. Extensive experimental results on
two benchmark datasets demonstrate the effectiveness and superiority of our
method.
