Knowledge graphs (KGs) can provide rich, structured information for recommendation systems as well as increase accuracy and perform explicit reasoning. Deep reinforcement learning (RL) has also sparked great interest in personalized recommendations. The combination of the two holds promise in carrying out interpretable causal inference procedures and improving the performance of graph-structured recommendation. However, most KG-based recommendation focus on rich semantic relationships between entities in a heterogeneous knowledge graph, and thus fail to fully make use of the image information corresponding to an entity. In order to address these issues, we proposed a novel Multi-modal Knowledge-aware Reinforcement Learning Network (MKRLN), which couples recommendation and interpretability by providing actual paths in multi-modal KG (MKG). The MKRLN can generate path representation by composing the structural and visual information of entities, and infers the underlying rational of agent-MKG interactions by leveraging the sequential dependencies within a path from the MKG. In addition, as KGs have too many attributes and entities, their combination with RL leads to too many action spaces and states in the reinforcement learning space, which complicates the search of action spaces. Furthermore, in order to solve this problem, we proposed a new hierarchical attention-path, which makes users focus their attention on the items they are interested in. This reduces the relations and entities in the KGs, which in turn reduces the action space and state in RL, shortens the path to the target entity, and improves the accuracy of recommendation. Our model has explicit explanation ability in knowledge and images. Finally, we extensively evaluated our model on several large-scale real-world benchmark datasets, and it yielded favorable results compared with state-of-the-art methods.

知识图谱 (KG) 可以为推荐系统提供丰富的结构化信息，并提高准确性和执行显式推理。深度强化学习 (RL) 也引发了对个性化推荐的极大兴趣。两者的结合在执行可解释的因果推理过程和提高图结构推荐的性能方面有希望。然而，大多数基于 KG 的推荐侧重于异构知识图中实体之间丰富的语义关系，无法充分利用实体对应的图像信息。为了解决这些问题，我们提出了一种新颖的多模态知识感知强化学习网络 (MKRLN)，它通过提供多模态 KG (MKG) 中的实际路径来将推荐和可解释性结合起来。MKRLN 可以通过组合实体的结构和视觉信息来生成路径表示，并通过利用来自 MKG 的路径内的顺序依赖性来推断代理与 MKG 交互的潜在合理性。此外，由于 KG 有太多的属性和实体，它们与 RL 的结合导致强化学习空间中的动作空间和状态过多，这使得动作空间的搜索变得复杂。此外，为了解决这个问题，我们提出了一种新的分层注意力路径，使用户将注意力集中在他们感兴趣的项目上。这减少了 KG 中的关系和实体，从而减少了动作空间和RL 中的状态，缩短到目标实体的路径，提高推荐的准确性。我们的模型在知识和图像方面具有明确的解释能力。最后，我们在几个大型真实世界基准数据集上广泛评估了我们的模型，与最先进的方法相比，它产生了良好的结果。