Explainable recommendation systems (ERSs) have attracted increasing attention from researchers, which generate high-quality recommendations with intuitive explanations to help users make appropriate decisions. However, most of the existing ERSs are designed with an offline setting, which can hardly adjust their models using the online feedback instantly for improved performance. To overcome the limitations of ERSs with the offline setting, we propose a novel online setting for ERSs and devise an effective model called O³ERS in this online setting, which can perform online learning with good scalability and rigorous theoretical guides for better online recommendations and online explanations. O³ERS also addresses two challenging problems in real scenarios, namely, the sparsity and delay of online explanations’ feedback as well as the partialness and insufficiency of online recommendations’ feedback. Specifically, O³ERS not only instantly leverages the knowledge learned from the recommendations’ feedback to adjust the sparse and delayed explanations’ feedback for better explanations but also utilizes a novel exploitation–exploration strategy that incorporates the explanations’ feedback to adjust the partial and insufficient recommendations’ feedback for better recommendations. Our theoretical analysis and empirical studies on one simulated and two real-world datasets show that our model outperforms the state-of-the-art models in online scenarios remarkably.

可解释的推荐系统（ERS）引起了研究人员的越来越多的关注，研究人员通过直观的解释生成高质量的推荐，以帮助用户做出适当的决策。但是，大多数现有ERS都设计为具有脱机设置，因此几乎无法立即使用在线反馈来调整其模型以提高性能。为克服离线环境下ERS的局限性，我们为ERS提供了一种新颖的在线环境，并在此在线环境中设计了一种称为O ³ ERS的有效模型，该模型可以进行在线学习，并具有良好的可扩展性和严格的理论指导，可提供更好的在线建议和在线说明。Ø ³ERS还解决了实际情况中的两个具有挑战性的问题，即在线解释反馈的稀疏性和延迟以及在线推荐反馈的局部性和不足性。具体来说，O ³ ERS不仅立即利用从建议反馈中获得的知识来调整稀疏和延迟的解释反馈，以更好地进行解释，而且还采用了一种新颖的开发-探索策略，该方法结合了解释反馈来调整部分和不足建议的反馈，以获得更好的建议。我们对一个模拟和两个真实数据集的理论分析和实证研究表明，在在线场景中，我们的模型明显优于最新模型。