@inproceedings{verma-etal-2025-empowering,
    title = "Empowering Recommender Systems using Automatically Generated Knowledge Graphs and Reinforcement Learning",
    author = "Verma, Ghanshyam  and
      Sarkar, Simanta  and
      Pillai, Devishree  and
      Chen, Huan  and
      McCrae, John Philip  and
      Perge, J{\'a}nos A.  and
      Sengupta, Shovon  and
      Buitelaar, Paul",
    editor = "Alam, Mehwish  and
      Tchechmedjiev, Andon  and
      Gracia, Jorge  and
      Gromann, Dagmar  and
      di Buono, Maria Pia  and
      Monti, Johanna  and
      Ionov, Maxim",
    booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge",
    month = sep,
    year = "2025",
    address = "Naples, Italy",
    publisher = "Unior Press",
    url = "https://preview.aclanthology.org/ldl-25-ingestion/2025.ldk-1.33/",
    pages = "328--339",
    ISBN = "978-88-6719-333-2",
    abstract = "23 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."
}