Vocational Education Information Technology Based on Cross-Attention Fusion Knowledge Map Recommendation Algorithm

Peng Jiang ()
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Peng Jiang: Department of Ideological and Political Education, Henan Vocational College of Agriculture, Zhengzhou 451450, P. R. China

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 03, 1-20

Abstract: With the rapid development of China’s economic development, the demand for technical talents in all walks of life is becoming more and more urgent. Therefore, the research on the intelligent method of vocational education information is becoming more and more important. In this research, the cross-attention fusion module and attention mechanism are introduced into the knowledge map recommendation algorithm to build an algorithm model. The attention mechanism is used to give corresponding attention to each neighbour node of the head node in the knowledge map, and a weight matrix is established to represent different importances of the additional information contained by each neighbour node, which further improves the interpretability of the recommendation. Finally, the model is analysed experimentally. The results show that CAF is superior to other algorithms in Recall and NDCG, which further verifies that attention mechanism plays a significant role in communication. It can be seen that CAF optimisation model is superior to other algorithms in many tests, and is superior to a similar algorithm MKR, which further verifies the effectiveness and superiority of cross-attention fusion module. The CAF model can still maintain its stability in the case of sparse data.

Keywords: Attention mechanism; knowledge graph; recommendation algorithm; vocational education (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219649223500077

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