A Knowledge Graph and Contrastive Learning-based Framework for Cross-Domain Book–Movie Recommendations with Explainability

An emerging approach called cross-domain recommendation transfers knowledge across heterogeneous areas, including books and movies, to improve the user experience. Existing methods often suffer from sparse interactions, semantic gaps across domains, and insufficient explainability. To address these limitations, we propose a novel Cross-Domain Knowledge Graph Contrastive (X-DKGC) unified framework that unifies knowledge graph modeling, contrastive representation learning, and explainable reasoning. First, a unified heterogeneous knowledge graph is constructed by integrating users, books, movies, genres, authors, directors, similarity links, and cross-domain semantic relations. Next, X-DKGC introduces two new modules: Multi-Hop Semantic Sampler (MHSS) and Dual-Domain Contrastive Learning (DDCL) to refine initial KG embeddings using intra-domain and cross-domain contrastive signals. A structure-aware regularizer is also formulated to preserve graph topology during training. The resulting embeddings support both recommendation and transparent explanation generation via multi-hop reasoning, semantic overlaps, and collaborative neighbors. Extensive experiments were conducted on both the original Goodreads–MovieLens dataset and the publicly available Amazon Review Data benchmark (Books and Movies and TV). The proposed framework achieved higher observed performance than the representative state-of-the-art baselines across both datasets, with statistical significance varying across datasets, metrics, and baseline comparisons. The proposed framework was also evaluated against KG-NeuCMF under identical experimental settings and achieved higher observed performance across the reported evaluation metrics.