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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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01999-9 |
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| Summary: | 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. |
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| ISSN: | 2045-2322 |