Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning

TL;DR

This paper presents two novel methods for personalized recommender systems in financial services, combining automatically generated knowledge graphs with reinforcement learning and explainability techniques to improve accuracy and interpretability.

Contribution

It introduces two approaches that integrate knowledge graphs with reinforcement learning and machine learning explainability, enhancing recommendation quality and transparency.

Findings

Improved recommendation accuracy through KG integration

Enhanced interpretability with Path Directed Reasoning and SHAP/ELI5

Effective personalization in financial services

Abstract

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.