DivCDSR: A Model-Agnostic Framework for Diverse Cross-Domain Sequential Recommendation

While Cross-Domain Sequential Recommendation (CDSR) has proven effective in mitigating data sparsity and enhancing accuracy, its impact on recommendation diversity remains largely unexplored. We are the first to reveal a counterintuitive phenomenon: while CDSR improves accuracy, it often comes at the cost of diversity, confining users to a narrower scope of interests. Through rigorous empirical experiments and theoretical analysis, we pinpoint two fundamental determinants driving this decline: (1) Domain Homogeneity, where excessive similarity between domains enforces preference redundancy; and (2) Information Asymmetry, where insufficient signal from the source domain fails to meaningfully perturb target-domain distributions. To address these challenges, we propose DivCDSR, a novel model-agnostic framework designed to enhance diversity in CDSR. Specifically, we introduce a dual-prototype semantic constraint mechanism that mitigates the homogenization trap via intra-domain clustering with orthogonalization and inter-domain separation. Furthermore, we devise a dual-guided diffusion module that augments source-domain information by generating new sequences, thereby resolving the information asymmetry issue. Extensive experiments on three public datasets demonstrate that our DivCDSR significantly enhances diversity metrics while maintaining or even improving recommendation accuracy simultaneously. %, offering a robust solution to the informational poverty inherent in conventional CDSR models. All datasets and code are available at https://github.com/Asuei-cs/DivCDSR.