Explainable Recommender Systems Through Reinforcement Learning and Knowledge Distillation on Knowledge Graphs

Recommender systems have evolved significantly in recent years, using advanced techniques such as explainable artificial intelligence, reinforcement learning, and graph neural networks to enhance both efficiency and transparency. This study presents a novel framework, XR2K2G (X for explainability, first R for recommender systems, the second R for reinforcement learning, first K for knowledge graph, the second K stands for knowledge distillation, and G for graph-based techniques), with the goal of developing a next-generation recommender system with a focus on careers empowerment. To optimize recommendations while ensuring sustainability and transparency, the proposed method integrates reinforcement learning with graph-based representations of career trajectories. Additionally, it incorporates knowledge distillation techniques to further refine the model’s performance by transferring knowledge from a larger model to a more efficient one. Our approach employs reinforcement learning algorithms, graph embeddings, and knowledge distillation to enhance recommendations by providing clear and comprehensible explanations for the recommendations. In this work, we discuss the technical foundations of the framework, deployment strategies, and its practical applicability in real-world career scenarios. The effectiveness and interpretability of our approach are demonstrated through experimental results.