  Current session-based recommender systems (SBRSs) mainly focus on maximizing
recommendation accuracy, while few studies have been devoted to improve
diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented
SBRSs perform in terms of diversity. Besides, the asserted "trade-off"
relationship between accuracy and diversity has been increasingly questioned in
the literature. Towards the aforementioned issues, we conduct a holistic study
to particularly examine the recommendation performance of representative SBRSs
w.r.t. both accuracy and diversity, striving for better understanding the
diversity-related issues for SBRSs and providing guidance on designing
diversified SBRSs. Particularly, for a fair and thorough comparison, we
deliberately select state-of-the-art non-neural, deep neural, and diversified
SBRSs, by covering more scenarios with appropriate experimental setups, e.g.,
representative datasets, evaluation metrics, and hyper-parameter optimization
technique. Our empirical results unveil that: 1) non-diversified methods can
also obtain satisfying performance on diversity, which might even surpass
diversified ones; and 2) the relationship between accuracy and diversity is
quite complex. Besides the "trade-off" relationship, they might be positively
correlated with each other, that is, having a same-trend (win-win or lose-lose)
relationship, which varies across different methods and datasets. Additionally,
we further identify three possible influential factors on diversity in SBRSs
(i.e., granularity of item categorization, session diversity of datasets, and
length of recommendation lists).
