Scalable multi-metric association rule learning for explainable book recommendations

Digital reading platforms have grown rapidly, increasing information overload and highlighting the need for efficient and transparent recommendation systems. This study presents a scalable hybrid framework that combines multi-metric association rule learning (ARL) with intelligent filtering strategies to provide clear, high-quality book recommendations at scale. Unlike traditional ARL-based recommenders that depend on a single metric or small datasets, our approach combines support, confidence, and lift measures to identify strong behavioral patterns while maintaining computational efficiency. The framework uses data-reduction strategies that select active users and high-impact items, transforming a sparse rating matrix into a dense, computationally tractable representation. Extensive experiments on a real-world dataset demonstrated that our method significantly outperforms collaborative filtering, neural models, and rule-mining baselines in precision, recall, and normalized discounted cumulative gain (NDCG). The resulting rules are inherently interpretable, enabling clear explanations for recommendations, which is a critical feature of modern personalized systems. This study demonstrates that ARL remains viable when designed with modern scalability constraints in mind, providing an explainable, efficient solution for digital libraries, online platforms, and large-scale recommender systems.