Abstract
Aiming at the poor recommendation effect of the traditional hybrid recommendation algorithm for educational courseware resources, a hybrid recommendation algorithm based on heterogeneous information fusion is proposed. Through the description of the characteristics of educational courseware resources, the attributes are mapped into the rating matrix, and the average values of the attributes of all the evaluated educational courseware resources are calculated. After the similar items are merged, the double attribute rating matrix is obtained. The modular matrix is used to process the sub factor sequence, and the mean value of the correlation coefficient corresponding to each sub factor sequence is calculated. This paper studies the coupling relationship between educational courseware resources, completes the modular processing of educational courseware resources, and realizes the recommendation of network hybrid information combined with the design of network hybrid information recommendation algorithm. The experimental results show that the hybrid recommendation algorithm based on heterogeneous information fusion can better solve the problem of low recommendation efficiency caused by sparse score matrix and “cold start”. The recommendation effect is better than the traditional collaborative recommendation algorithm, and the quality is higher.
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References
Huayue, C.: The study on interesting mining based on topic model for web educational resource recommendation. Adv. Inf. Sci. Serv. Sci. 4(2), 275–281 (2012)
Manouselis, N., Vuorikari, R., Assche, F.V.: Collaborative recommendation of e-learning resources: an experimental investigation. J. Comput. Assist. Learn. 26(4), 227–242 (2010)
Lau, B.Y., Lee, C.S., Singh, Y.P.: A folksonomy-based lightweight resource annotation metadata schema for personalized hypermedia learning resource delivery. Interact. Learn. Environ. 23(1), 79–105 (2015)
Luo, J., Dong, F., Cao, J., et al.: A context-aware personalized resource recommendation forpervasive learning. Clust. Comput. 13(2), 213–239 (2010)
Zhu, H., Liu, Y., Tian, F., et al.: A cross-curriculum video recommendation algorithm based on a video-associated knowledge map. IEEE Access 1 (2018)
Liu, S., Lu, M., Li, H., et al.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. 10, 120 (2019)
Liu, S., Li, Z., Zhang, Y., et al.: Introduction of key problems in long-distance learning and training. Mob. Netw. Appl. 24(1), 1–4 (2019)
Liu, S., Glowatz, M., Zappatore, M., et al. (eds.): e-Learning, e-Education, and Online Training, pp. 1–374. Springer, Heidelberg (2018)
Corbellini, A., Mateos, C., Godoy, D., et al.: An architecture and platform for developing distributed recommendation algorithms on large-scale social networks. J. Inf. Sci. 41(5), 686–704 (2015)
Mahboob, V.A., Jalali, M., Jahan, M.V., et al.: Swallow: resource and tag recommender system based on heat diffusion algorithm in social annotation systems. Comput. Intell. 33(1), 99–118 (2017)
Ferreira, L.G., Barbosa, J., Gluz, J.C., et al.: Using learner group profiles for content recommendation in ubiquitous environments. Int. J. Inf. Commun. Technol. Educ. Official Publ. Inf. Resour. Manag. Assoc. 16(4), 1–19 (2020)
Chen, H., Yin, C., Li, R., et al.: Enhanced learning resource recommendation based on online learning style model. Tsinghua Sci. Technol. 25(3), 348–356 (2020)
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This paper is a scientific research fund project of Hunan Provincial Department of Education (project number: 20c0218).
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wu, Sb., Yang, Y. (2021). Research on Hybrid Recommendation Algorithm of Educational Courseware Resources Based on Heterogeneous Information Fusion. In: Fu, W., Liu, S., Dai, J. (eds) e-Learning, e-Education, and Online Training. eLEOT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 390. Springer, Cham. https://doi.org/10.1007/978-3-030-84386-1_26
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DOI: https://doi.org/10.1007/978-3-030-84386-1_26
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