论文标题
关系增强的土匪
Relational Boosted Bandits
论文作者
论文摘要
近年来，上下文匪徒算法在现实世界的用户交互问题中已成为必不可少的。但是，这些算法依靠上下文作为属性值表示，这使得它们对于像社交网络这样的现实世界中的界面是不可行的。我们提出了基于（关系）增强树的关系域的关系增强的匪徒（RB2），有关关系域的可位生物匪徒算法。 RB2使我们能够学习可解释和可解释的模型，因为关系表示的描述性质。我们从经验上证明了RB2对诸如链接预测，关系分类和建议等任务的有效性和解释性。
Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representation, which makes them unfeasible for real-world domains like social networks are inherently relational. We propose Relational Boosted Bandits(RB2), acontextual bandits algorithm for relational domains based on (relational) boosted trees. RB2 enables us to learn interpretable and explainable models due to the more descriptive nature of the relational representation. We empirically demonstrate the effectiveness and interpretability of RB2 on tasks such as link prediction, relational classification, and recommendations.
