Modern Algorithmic Strategies in Cross-Domain Recommender Systems: A Comparative Review of Knowledge Transfer, Representation Learning, and Evaluation Trends (2018–2025)

Cross-domain recommender systems (CDRS) have become an important research direction for addressing sparsity, cold-start, and fragmented user-behavior problems that cannot be solved effectively within a single domain. In recent years, the field has expanded rapidly from early neural transfer models into a broad landscape that includes graph-based methods, sequential recommendation, adversarial alignment, contrastive learning, disentanglement, multimodal transfer, federated recommendation, and emerging LLM-assisted approaches. This paper presents a comparative review of modern algorithmic strategies in CDRS published between 2018 and 2025. [21, 23, 32, 54] Unlike reviews that summarize the literature only chronologically, this study organizes prior work by algorithm family and compares how different methods perform cross-domain transfer, what assumptions they rely on, what types of datasets and user signals they use, and where their major strengths and limitations lie. The review integrates representative journal and conference studies from major venues and presents a taxonomy of modern CDRS methods, cross-family comparison tables, a method-evolution timeline, a dataset-and-evaluation pipeline view, and a research-gap map. [57, 59] The comparative analysis shows that graph and knowledge-graph methods are particularly strong in structurally rich and sparse settings, sequential methods are especially effective for temporally evolving behavior, contrastive and disentanglement-based approaches improve robustness and negative-transfer control, multimodal methods expand transfer under weak overlap through richer semantic information, and federated methods address growing privacy and deployment requirements. The review also finds that no single family is universally optimal, and that future progress is likely to depend on hybrid, condition-aware models that combine selective transfer, semantic enrichment, robustness, and distributed learning. [21, 23, 32, 54] The paper concludes by identifying major open challenges, including weak overlap, domain heterogeneity, benchmark inconsistency, scalability, explainability, and privacy-aware deployment. It argues that the next stage of CDRS research will be shaped by more reliable selective-transfer mechanisms, multimodal and foundation-model integration, continual adaptation, and stronger evaluation standards for real-world recommendation environments. [35, 53, 55, 56]