基于交叉关注融合的职业教育信息技术知识地图推荐算法

Peng Jiang
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引用次数: 0

摘要

随着中国经济发展的快速发展,各行各业对技术型人才的需求越来越迫切。因此,对职业教育信息智能化方法的研究显得越来越重要。本研究将交叉注意融合模块和注意机制引入到知识地图推荐算法中,构建算法模型。利用关注机制对知识图谱中头节点的每个邻居节点给予相应的关注,并建立权重矩阵来表示每个邻居节点所包含的附加信息的不同重要度,进一步提高了推荐的可解释性。最后,对模型进行了实验分析。结果表明,CAF在Recall和NDCG方面优于其他算法,这进一步验证了注意机制在沟通中起着重要作用。从多次测试中可以看出,CAF优化模型优于其他算法,并优于同类算法MKR,进一步验证了交叉注意融合模块的有效性和优越性。在数据稀疏的情况下,CAF模型仍能保持其稳定性。
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Vocational Education Information Technology Based on Cross-Attention Fusion Knowledge Map Recommendation Algorithm
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.
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