Empowering recommender systems using automatically generated Knowledge
  Graphs and Reinforcement Learning
Ghanshyam Verma Shovon Sengupta Simon Simanta Huan Chen Janos A. Perge Devishree Pillai John P. McCrae Paul Buitelaar
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.