Domain-aware self-prompting for cross-domain sequential recommendations with natural language explanations

Abstract

• We propose DASP, a novel domain-aware prompting framework for cross-domain sequential recommendations with natural explanations. • Addresses domain gaps through contrastive learning and meta-learned adapters for efficient domain adaptation. • DASP outperforms baselines with 10.7 % HR@10 and 10.5 % NDCG@10 improvements with 17.3 % better explanation quality. • DASP reduces training time by 54 % using a lightweight architecture and frozen large language models. Cross-domain sequential recommendation faces persistent challenges in addressing domain shift, data sparsity, and the trade-off between performance, efficiency, and explainability. Existing methods often struggle with inefficient cross-domain adaptation or fail to generate coherent explanations that bridge user preferences across domains. To overcome these limitations, we propose Domain-Aware Self-Prompting (DASP) , a novel framework that integrates cross-domain recommendation with natural language explanation generation. DASP introduces three key innovations: (1) a domain-invariant self-prompt generator that captures shared user preferences via contrastive alignment across domains; (2) lightweight domain adapters with meta-learned initialization for parameter-efficient adaptation to target domains; and (3) a cross-domain explanation generator that grounds recommendations in semantically aligned multi-domain prompts using large language models. Extensive experiments on Amazon Movie-Book and Food-Kitchen datasets demonstrate DASP’s superiority, achieving 10.7 % and 10.5 % improvements in HR@10 and NDCG@10 over state-of-the-art baselines on the Movie-Book dataset, while reducing training time by 54 % compared to full large language models fine-tuning approaches. Qualitative and quantitative analyses validate DASP’s ability to generate interpretable explanations that link cross-domain preferences, offering a scalable and trustworthy solution for cross-domain sequential recommendation. Our work bridges critical gaps in efficiency, adaptability, and explainability for real-world multi-domain recommendation systems.