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).

翻译：当前基于会话的推荐系统主要关注最大化推荐准确性，而很少有研究致力于在准确性之外提升多样性。同时，以准确性为导向的基于会话的推荐系统在多样性方面的表现尚不明确。此外，文献中关于准确性与多样性之间“权衡”关系的断言日益受到质疑。针对上述问题，我们开展了一项整体性研究，专门考察代表性基于会话的推荐系统在准确性和多样性两方面的推荐性能，旨在更好地理解基于会话的推荐系统中与多样性相关的问题，并为设计多样化的基于会话的推荐系统提供指导。特别是，为了进行公平且全面的比较，我们精心选择了最先进的非神经网络、深度神经网络及多样化的基于会话的推荐系统，通过适当的实验设置（例如代表性数据集、评估指标和超参数优化技术）覆盖更多场景。我们的实证结果揭示：1）非多样化方法也能在多样性方面获得令人满意的性能，甚至可能超越多样化方法；2）准确性与多样性之间的关系相当复杂。除了“权衡”关系外，它们之间可能呈正相关，即具有同向趋势（双赢或双输）关系，且这种关系因方法和数据集而异。此外，我们进一步识别出基于会话的推荐系统中影响多样性的三个可能因素（即物品分类的粒度、数据集的会话多样性及推荐列表的长度）。