Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach
Abstract Enterprise knowledge retrieval faces challenges like sparse data and inefficient cross-domain knowledge transfer, hindering traditional methods. To address this, we develop a cross-domain recommendation model (CDR-VAE), combining a hybrid autoencoder with domain alignment, and test its effe...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01999-9 |
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| author | Ting Li |
| author_facet | Ting Li |
| author_sort | Ting Li |
| collection | DOAJ |
| description | Abstract Enterprise knowledge retrieval faces challenges like sparse data and inefficient cross-domain knowledge transfer, hindering traditional methods. To address this, we develop a cross-domain recommendation model (CDR-VAE), combining a hybrid autoencoder with domain alignment, and test its effectiveness on an enterprise dataset and the Movies&Books benchmark. At a top-5 recommendation length, CDR-VAE scores HR = 0.642, Recall = 0.432, NDCG = 0.715, outperforming existing models. Removing shared latent representations reduces HR to 0.701, proving their necessity for cross-domain learning. In enterprise applications, high-activity users favor technical reports (0.903), while low-activity users shift toward cross-domain content like industry standards (0.701), confirming the model’s robustness in sparse scenarios. CDR-VAE successfully tackles sparsity and cross-domain barriers, advancing enterprise knowledge management. This work provides theoretical and practical insights for deep learning-based recommendation systems in data-scarce environments. |
| format | Article |
| id | doaj-art-3c8cb7ae5e904734997579fded045dba |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3c8cb7ae5e904734997579fded045dba2025-08-20T02:34:17ZengNature PortfolioScientific Reports2045-23222025-05-0115111510.1038/s41598-025-01999-9Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approachTing Li0Shanghai Keyao Industrial Co. Ltd.Abstract Enterprise knowledge retrieval faces challenges like sparse data and inefficient cross-domain knowledge transfer, hindering traditional methods. To address this, we develop a cross-domain recommendation model (CDR-VAE), combining a hybrid autoencoder with domain alignment, and test its effectiveness on an enterprise dataset and the Movies&Books benchmark. At a top-5 recommendation length, CDR-VAE scores HR = 0.642, Recall = 0.432, NDCG = 0.715, outperforming existing models. Removing shared latent representations reduces HR to 0.701, proving their necessity for cross-domain learning. In enterprise applications, high-activity users favor technical reports (0.903), while low-activity users shift toward cross-domain content like industry standards (0.701), confirming the model’s robustness in sparse scenarios. CDR-VAE successfully tackles sparsity and cross-domain barriers, advancing enterprise knowledge management. This work provides theoretical and practical insights for deep learning-based recommendation systems in data-scarce environments.https://doi.org/10.1038/s41598-025-01999-9Enterprise knowledge retrievalCross-domain recommendationSparse data scenariosDeep generative modelCDR-VAE |
| spellingShingle | Ting Li Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach Scientific Reports Enterprise knowledge retrieval Cross-domain recommendation Sparse data scenarios Deep generative model CDR-VAE |
| title | Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach |
| title_full | Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach |
| title_fullStr | Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach |
| title_full_unstemmed | Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach |
| title_short | Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach |
| title_sort | enhancing enterprise knowledge retrieval via cross domain deep recommendation a sparse data approach |
| topic | Enterprise knowledge retrieval Cross-domain recommendation Sparse data scenarios Deep generative model CDR-VAE |
| url | https://doi.org/10.1038/s41598-025-01999-9 |
| work_keys_str_mv | AT tingli enhancingenterpriseknowledgeretrievalviacrossdomaindeeprecommendationasparsedataapproach |