Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning
This addresses the need for transparent recommender systems in financial services to enhance customer engagement, though it appears incremental by combining existing methods like RL and XGBoost with knowledge graphs.
The study tackled personalized article recommendation in financial services by proposing two interpretable knowledge graph-based approaches, resulting in improved recommendation accuracy and interpretable insights.
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.