Citation

Ayemowa, Matthew O. and Ibrahim, Roliana and Zakaria, Noor Hidayah and Kamal, Shahid (2026) Generative artificial intelligence model for knowledge transfer in data-sparse cross-domain recommender systems. Discover Computing, 29 (1). ISSN 2948-2992

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Abstract

The rapid growth of online digital platforms has significantly increased the need for recommender systems (RSs) that can deliver personalized content to users. Crossdomain 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 crossdomain 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 targetdomain data by producing high-quality synthetic samples, thereby mitigating the impact of data sparsity problems. Extensive experiments are conducted on four realworld 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.

Item Type:	Article
Uncontrolled Keywords:	Data sparsity, Recommender systems, Auxiliary information
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 02:10
Last Modified:	04 Sep 2026 02:10
URII:	http://shdl.mmu.edu.my/id/eprint/16679

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