Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning

• URL: http://arxiv.org/abs/2307.04996v1
• Date: Tue, 11 Jul 2023 03:24:54 GMT
• Title: Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning
• Authors: Ghanshyam Verma, Shovon Sengupta, Simon Simanta, Huan Chen, Janos A. Perge, Devishree Pillai, John P. McCrae, Paul Buitelaar
• Abstract summary: We present two knowledge graph-based approaches for personalized article recommendations for a set of customers. The first approach employs Reinforcement Learning and the second approach uses the XGBoost algorithm for recommending articles. Both approaches make use of a KG generated from both structured (tabular data) and unstructured data.
• Score: 3.6587485160470226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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