Multi-Dimensional Correlative Recommendation and Adaptive Clustering via Incremental Tensor Decomposition for Sustainable Smart Education

Online education and e-learning have vigorously sprung up and produced massive educational data in a streaming way. It is very challenging to acquire the appropriate learning resources and the suitable learning partners from the streaming-updated educational big data. This article aims to provide su...

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Vydané v:	IEEE transactions on sustainable computing Ročník 5; číslo 3; s. 389 - 402
Hlavní autori:	Liu, Huazhong, Ding, Jihong, Yang, Laurence T., Guo, Yimu, Wang, Xiaokang, Deng, Anyuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.07.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive algorithms adaptive clustering adaptive community recommendation Adaptive learning Algorithms Big Data CAI Clustering Clustering algorithms Computer assisted instruction Correlation analysis Decomposition Distance learning Education educational big data Educational services Electronic learning Euclidean distance incremental tensor decomposition Machine learning Mathematical analysis multi-dimensional correlative analysis personalized resource recommendation Sensors Sustainable development Sustainable smart education Tensors
ISSN:	2377-3782, 2377-3790
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Abstract	Online education and e-learning have vigorously sprung up and produced massive educational data in a streaming way. It is very challenging to acquire the appropriate learning resources and the suitable learning partners from the streaming-updated educational big data. This article aims to provide sustainable smart educational services including precise personalized recommendation and adaptive clustering under different contexts by correlatively analyzing the global educational data from multiple dimensions via incremental tensor decomposition. First, a tensor-based recommendation and service framework for the streaming educational big data is developed. Then three local tensors concerning learners, resources, and learning records are constructed and further fused to an integrated global learner-resource tensor. Afterward, we present an incremental tensor-based correlative analysis and personalized recommendation (ITCA-PR) algorithm to recommend appropriate resources under various contexts. Besides, we also propose an incremental tensor-based adaptive clustering and community recommendation (ITAC-CR) algorithm to recommend suitable learning partners under various contexts and accordingly construct adaptive learning communities. Extensive experimental results demonstrate that the ITCA-PR algorithm outperforms state-of-the-art improved collaborative filtering algorithms by F-score measurement and the ITAC-CR algorithm has a better clustering performance by DVI measurement. These precise educational services will promote the development of sustainable smart education.
AbstractList	Online education and e-learning have vigorously sprung up and produced massive educational data in a streaming way. It is very challenging to acquire the appropriate learning resources and the suitable learning partners from the streaming-updated educational big data. This article aims to provide sustainable smart educational services including precise personalized recommendation and adaptive clustering under different contexts by correlatively analyzing the global educational data from multiple dimensions via incremental tensor decomposition. First, a tensor-based recommendation and service framework for the streaming educational big data is developed. Then three local tensors concerning learners, resources, and learning records are constructed and further fused to an integrated global learner-resource tensor. Afterward, we present an incremental tensor-based correlative analysis and personalized recommendation (ITCA-PR) algorithm to recommend appropriate resources under various contexts. Besides, we also propose an incremental tensor-based adaptive clustering and community recommendation (ITAC-CR) algorithm to recommend suitable learning partners under various contexts and accordingly construct adaptive learning communities. Extensive experimental results demonstrate that the ITCA-PR algorithm outperforms state-of-the-art improved collaborative filtering algorithms by F-score measurement and the ITAC-CR algorithm has a better clustering performance by DVI measurement. These precise educational services will promote the development of sustainable smart education.
Author	Wang, Xiaokang Yang, Laurence T. Deng, Anyuan Liu, Huazhong Guo, Yimu Ding, Jihong
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SubjectTerms	Adaptive algorithms adaptive clustering adaptive community recommendation Adaptive learning Algorithms Big Data CAI Clustering Clustering algorithms Computer assisted instruction Correlation analysis Decomposition Distance learning Education educational big data Educational services Electronic learning Euclidean distance incremental tensor decomposition Machine learning Mathematical analysis multi-dimensional correlative analysis personalized resource recommendation Sensors Sustainable development Sustainable smart education Tensors
Title	Multi-Dimensional Correlative Recommendation and Adaptive Clustering via Incremental Tensor Decomposition for Sustainable Smart Education
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