Generative artificial intelligence model for knowledge transfer in data-sparse cross-domain recommender systems

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The rapid growth of online digital platforms has significantly increased the need for recommender systems (RSs) that can deliver personalized content to users. Cross-domain recommender systems (CDRS) have emerged as promising solution to the limitations of single-domain models by incorporating user preferences, interaction histories, and item features from a source domain to enhance recommendations accuracy in a sparse target domain. However, effective transfer of knowledge from source domain to the target domain remains a challenging task due to differences in distributions of data, domain inconsistencies, and variations in user behavior. In this study, we propose a sparsity-aware generative adversarial networks-based cross-domain recommender system, named SPARGAN. The proposed model facilitates flexible and effective knowledge transfer by learning domain-invariant latent representations and generating realistic synthetic user-item interactions. SPARGAN incorporates adversarial learning and a domain-confusion loss to align user-item feature distributions between the source and target domains while preserving personalized user preferences. Additionally, the generator enhances the target-domain data by producing high-quality synthetic samples, thereby mitigating the impact of data sparsity problems. Extensive experiments are conducted on four real-world datasets: MovieLens, Amazon, Yelp, and Book-crossing. The experimental results demonstrate that SPARGAN consistently outperforms baseline methods in both top-N recommendation and rating prediction tasks, achieving superior performance in terms of Recall, Precision, RMSE, and F1-score under extreme sparsity conditions. Overall, this study highlights the effectiveness of adversarial learning for cross-domain knowledge transfer and provides foundation for future research on multi-source domain adaptation in cross-domain recommender systems with Gen AI models.