7b2c020c-6d48-440a-8ed3-d2e81e80820f20230518045908209naun:naunmdt@crossref.orgMDT DepositInternational Journal of Education and Information Technologies2074-131610.46300/9109http://www.naun.org/cms.action?id=3037224202322420231710.46300/9109.2023.17https://npublications.com/journals/educationinformation/2023.phpChengyunLiSchool of Ph.D. in English Language, Lyceum of the Philippines University, PhilippinesToday, with the development of intelligent media, the foreign communication and teaching activities of the Chinese central plains culture should actively seek experiences that can be learned from, establish a multi-channel foreign communication mode, and then promote the Chinese central plains culture to go out of the country and into the world better. The study improves the collaborative filtering recommendation algorithm and the joint matrix decomposition algorithm based on the theory of migration learning, aiming to improve the learning to optimize the resource recommendation system by calculating the user similarity and establishing the user preference-resource feature matrix. The experimental results show that the average absolute error and root mean square error of the improved algorithms are lower than those of other algorithms, proving that the optimized algorithms improve the accuracy and efficiency of resource recommendation in the foreign communication and teaching activities of the Chinese central plains culture while operating stably and with wide applicability on the recommendation system.5182023518202351586https://npublications.com/journals/educationinformation/2023/a122008-006(2023).pdf10.46300/9109.2023.17.6https://npublications.com/journals/educationinformation/2023/a122008-006(2023).pdf10.1016/j.apenergy.2019.03.192L. Li, Y. Shan, Y. Lei, S Wu, X Yu, X Lin, and Y Chen, “Decoupling of economic growth and emissions in China's cities: A case study of the Central Plains urban agglomeration,” Appl. Energy, vol. 244, pp. 36-45. 2019. 10.18653/v1/n19-5004S. Ruder, M. E. Peters, S. Swayamdipta, and T. Wolf, “Transfer learning in natural language processing,” Proc. 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Tutorials. pp. 15-18, 2019. 10.1016/j.media.2019.03.009V. Cheplygina, M. de Bruijne, and J. P. W. Pluim, “Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis,” Med. Image Anal., vol. 54, pp. 280-296, 2019. Y. Yu, “Witness the power of culture 'Our China Stories' media-integrated cultural communication activities launched in the UK,” China World Cultural Exch., no. 4, pp. 42-44, 2022. 10.1002/wmh3.522W. F. Mohammed, and T. B. Adekunle, “Communication in context: How culture, structure, and agency shape health and risk communication about COVID-19 in Ghana,” World Med. Health Policy, vol. 14, no. 2, pp. 432-439, 2022. 10.2308/tar-2020-0198C. A. Proell, Y. P. Zhou, and M. W. Nelson, “It's not only what you say... how communication style and team culture affect audit issue follow-up and auditor performance evaluations,” Accounting Rev., vol. 97, no. 2, pp. 373-395, 2022. 10.53106/160792642022012301014X. Liang, and J. S. Yin, “Recommendation algorithm for equilibrium of teaching resources in physical education network based on trust relationship,” J. Internet Technol., vol. 23, no. 1, pp. 133-141, 2022. 10.1016/j.eswa.2022.116695T. Kaya, and C. Kaleli, “A novel top-n recommendation method for multi-criteria collaborative filtering,” Expert Syst. Appl., vol. 198, pp. 116695.1-116695.12, 2022. 10.1016/j.ins.2021.12.059C. V. Nguyen, K. H. Le, A. M. Tran, Q. H. Pham, and B. T. Nguyen, “Learning for amalgamation: A multi-source transfer learning framework for sentiment classification,” Inf. Sci. Int. J., vol. 590, pp. 1-14, 2022. 10.1016/j.ins.2022.01.064Z. C. Sun, B. Wu, Y. Wang, and Y. Ye, “Sequential graph collaborative filtering,” Inf. Sci. Int. J., vol. 592, pp. 244-260, 2022. 10.1016/j.gsf.2021.101315G. C. Hao, J. Guo, W. Zhang, Y. Chen, and D. Ayuen, “High-precision chaotic radial basis function neural network model: Data forecasting for the Earth electromagnetic signal before a strong earthquake,” Geosci. Front., vol. 13, no. 1, pp. 364-373, 2022. 10.1109/jas.2021.1004362K. Zhu, C. P. Yu, and Y. M. Wan, “Recursive least squares identification with variable-direction forgetting via oblique projection decomposition,” J. Automation, vol. 9, no. 3, pp. 547-555, 2022.