We explore the semantic-rich structured information derived from the knowledge graphs (KGs) associated with the user-item interactions and aim to reason out the motivations behind each successful purchase behavior. Existing works on KGs-based explainable recommendations focus purely on path reasoning based on current user-item interactions, which generally result in the incapability of conjecturing users’ subsequence preferences. Considering this, we attempt to model the KGs-based explainable recommendation in sequential settings. Specifically, we propose a novel architecture called Reinforced Sequential Learning with Gated Recurrent Unit (RSL-GRU), which is composed of a Reinforced Path Reasoning Network (RPRN) component and a GRU component. RSL-GRU takes users’ sequential behaviors and their associated KGs in chronological order as input and outputs potential top-N items for each user with appropriate reasoning paths from a global perspective. Our RPRN features a remarkable path reasoning capacity, which is regulated by a user-conditioned derivatively action pruning strategy, a soft reward strategy based on an improved multi-hop scoring function, and a policy-guided sequential path reasoning algorithm. Experimental results on four of Amazon’s large-scale datasets show that our method achieves excellent results compared with several state-of-the-art alternatives.

我们探索从与用户-项目交互相关的知识图 (KG) 派生的语义丰富的结构化信息，旨在推理出每个成功购买行为背后的动机。基于 KGs 的可解释推荐的现有工作完全专注于基于当前用户-项目交互的路径推理，这通常导致无法推测用户的子序列偏好。考虑到这一点，我们尝试在顺序设置中对基于 KG 的可解释推荐进行建模。具体来说，我们提出了一种称为带门控循环单元的强化顺序学习 (RSL-GRU) 的新架构，它由强化路径推理网络 (RPRN) 组件和 GRU 组件组成。从全局角度来看，每个用户都有N 个项目，具有适当的推理路径。我们的 RPRN 具有卓越的路径推理能力，该能力由用户条件的衍生动作修剪策略、基于改进的多跳评分函数的软奖励策略和策略引导的顺序路径推理算法调节。在亚马逊的四个大规模数据集上的实验结果表明，与几种最先进的替代方法相比，我们的方法取得了出色的结果。