Intelligent push method of agricultural talents teaching resources based on association rule

Qi Wang; Guanghai Li

doi:10.1117/12.2680665

16 June 2023 Intelligent push method of agricultural talents teaching resources based on association rule

Qi Wang, Guanghai Li

Proceedings Volume 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023); 1270213 (2023) https://doi.org/10.1117/12.2680665
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 2023, Changsha, China

Abstract

With the development of agricultural science and technology, there are problems of information overload and data explosion in the process of finding educational resources through the network. This paper selects talent training in the field of agriculture as the goal to explore the intelligent push of network resources in the field of agricultural education. Combined with the establishment of collaborative association rules and the AutoSVD hybrid recommendation model, the K-Means algorithm was adopted to cluster the agricultural teaching resources information in the teaching resources retrieval model obtained in the early stage. Different types of resource clusters were obtained, and the resources with the most user preferences in each cluster were also obtained. Finally, the users in the same cluster were recommended. RMSE and MAE are calculated for comparison. Experiments verify that the performance of the hybrid model proposed in this paper is superior to that of the current mainstream models. The development and exploration of the theory of personalized learning will be beneficial from it. Thus, this study has some innovations.

Citation Download Citation

Qi Wang and Guanghai Li "Intelligent push method of agricultural talents teaching resources based on association rule", Proc. SPIE 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 1270213 (16 June 2023); https://doi.org/10.1117/12.2680665

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KEYWORDS

Machine learning

Agriculture

Education and training

Data modeling

Matrices

Cognitive modeling

Deep learning

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