Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach

• Published in: Scientific reports Vol. 15; no. 1; pp. 17507 - 15
• Main Author: Li, Ting
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Accuracy; CDR-VAE; Cross-domain recommendation; Datasets; Decision making; Deep generative model; Deep learning; Efficiency; Enterprise knowledge retrieval; Graphs; Humanities and Social Sciences; Knowledge management; Knowledge representation; Literature reviews; Methods; multidisciplinary; Optimization; Science; Science (multidisciplinary); Semantics; Sparse data scenarios; Enterprise knowledge retrieval; Cross-domain recommendation; Deep generative model; CDR-VAE; Sparse data scenarios

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...