  Session-based recommender systems (SBRSs) have become extremely popular in
view of the core capability of capturing short-term and dynamic user
preferences. However, most SBRSs primarily maximize recommendation accuracy but
ignore user minor preferences, thus leading to filter bubbles in the long run.
Only a handful of works, being devoted to improving diversity, depend on unique
model designs and calibrated loss functions, which cannot be easily adapted to
existing accuracy-oriented SBRSs. It is thus worthwhile to come up with a
simple yet effective design that can be used as a plugin to facilitate existing
SBRSs on generating a more diversified list in the meantime preserving the
recommendation accuracy. In this case, we propose an end-to-end framework
applied for every existing representative (accuracy-oriented) SBRS, called
diversified category-aware attentive SBRS (DCA-SBRS), to boost the performance
on recommendation diversity. It consists of two novel designs: a model-agnostic
diversity-oriented loss function, and a non-invasive category-aware attention
mechanism. Extensive experiments on three datasets showcase that our framework
helps existing SBRSs achieve extraordinary performance in terms of
recommendation diversity and comprehensive performance, without significantly
deteriorating recommendation accuracy compared to state-of-the-art
accuracy-oriented SBRSs.
