利用自动生成的知识图谱和强化学习增强推荐系统
Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning
AI总结：
本文提出两种基于知识图谱和强化学习的方法，用于金融服务中的个性化文章推荐，兼顾准确性与可解释性。
摘要：
个性化推荐系统在直接营销中扮演着至关重要的角色，尤其是在金融服务领域，提供相关内容可以增强客户参与度并促进明智决策。本研究探索了基于可解释知识图谱（KG）的推荐系统，提出了两种不同的方法，用于在一家跨国金融服务公司中进行个性化文章推荐。第一种方法利用强化学习（RL）遍历由结构化（表格）和非结构化（文本）数据构建的知识图谱，通过路径定向推理（PDR）实现可解释性。第二种方法采用XGBoost算法，并结合SHAP和ELI5等事后可解释性技术来增强透明度。通过将机器学习与自动生成的知识图谱相结合，我们的方法不仅提高了推荐准确性，还提供了可解释的洞察，促进了客户关系管理中更明智的决策。
英文摘要：
Personalized recommender systems play a crucial role in direct marketing, particularly in financial services, where delivering relevant content can enhance customer engagement and promote informed decision-making. This study explores interpretable knowledge graph (KG)-based recommender systems by proposing two distinct approaches for personalized article recommendations within a multinational financial services firm. The first approach leverages Reinforcement Learning (RL) to traverse a KG constructed from both structured (tabular) and unstructured (textual) data, enabling interpretability through Path Directed Reasoning (PDR). The second approach employs the XGBoost algorithm, with post-hoc explainability techniques such as SHAP and ELI5 to enhance transparency. By integrating machine learning with automatically generated KGs, our methods not only improve recommendation accuracy but also provide interpretable insights, facilitating more informed decision-making in customer relationship management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
