Personalized recommendations have a growing importance in direct marketing, which motivates research to enhance customer experiences by knowledge graph (KG) applications. For example, in financial services, companies may benefit from providing relevant financial articles to their customers to cultivate relationships, foster client engagement and promote informed financial decisions. While several approaches center on KG-based recommender systems for improved content, in this study we focus on interpretable KG-based recommender systems for decision making.To this end, we present two knowledge graph-based approaches for personalized article recommendations for a set of customers of a large multinational financial services company. The first approach employs Reinforcement Learning and the second approach uses the XGBoost algorithm for recommending articles to the customers. Both approaches make use of a KG generated from both structured (tabular data) and unstructured data (a large body of text data).Using the Reinforcement Learning-based recommender system we could leverage the graph traversal path leading to the recommendation as a way to generate interpretations (Path Directed Reasoning (PDR)). In the XGBoost-based approach, one can also provide explainable results using post-hoc methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I am Five).Importantly, our approach offers explainable results, promoting better decision-making. This study underscores the potential of combining advanced machine learning techniques with KG-driven insights to bolster experience in customer relationship management.

翻译：个性化推荐在直接营销中的重要性日益增长，这推动了通过知识图谱（KG）应用来提升客户体验的研究。例如，在金融服务领域，公司可通过向客户推荐相关的金融文章来培养关系、促进客户参与并推动明智的财务决策。尽管已有多种方法侧重于基于KG的推荐系统以改进内容质量，但本研究聚焦于可解释的基于KG的推荐系统以辅助决策。为此，我们针对一家大型跨国金融服务公司的客户群体，提出了两种基于知识图谱的个性化文章推荐方法。第一种方法采用强化学习，第二种方法使用XGBoost算法来向客户推荐文章。两种方法均利用了从结构化数据（表格数据）和非结构化数据（大量文本数据）中生成的KG。通过基于强化学习的推荐系统，我们能够利用生成推荐结果的图遍历路径来提供解释（路径导向推理，PDR）。在基于XGBoost的方法中，也可通过SHAP（SHapley Additive exPlanations）和ELI5（Explain Like I am Five）等事后解释方法提供可解释的结果。重要的是，我们的方法提供了可解释的结果，从而促进了更优的决策制定。本研究凸显了将先进机器学习技术与KG驱动的洞察相结合，以增强客户关系管理体验的潜力。