Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 32, Issue 5, Pages 827-840Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2895033
Keywords
Electronic learning; Recommender systems; Uncertainty; Collaboration; Hafnium; Computational modeling; Data models; Personalized e-learning; adaptive and intelligent educational systems; hybrid recommendation; influence model; self-organization; recommender system
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Funding
- National Natural Science Foundation of China [61370137]
- National 973 Project of China [2012CB720702]
- Ministry of Education China Mobile Research Foundation Project [2016/2-7]
- Major Science and Technology Project of Press and Publication [GAPP ZDKJ BQ/01]
- Beijing Municipal Party Committee [Z171100004417031]
- Beijing Municipal Government Key Work and District Government Emergency Project [Z171100004417031]
- Fundamental Research Funds for Beijing University of Civil Engineering and Architecture [X18070, X18044, X18065]
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In e-learning recommender systems, interpersonal information between learners is very scarce, which makes it difficult to apply collaborative filtering (CF) techniques to achieve recommendations. In this study, we propose a hybrid filtering recommendation approach ($SI-IFL$SI-IFL) combining learner influence model (LIM), self-organization based (SOB) recommendation strategy, and sequential pattern mining (SPM) together for recommending learning objects (LOs) to learners. The method works as follows: (i) LIM is applied to acquire the interpersonal information by computing the influence that a learner exerts on others. LIM consists of learner similarity, knowledge credibility, and learner aggregation, meanwhile, LIM is independent of ratings. Furthermore, to address the uncertainty and fuzzy natures of learners, intuitionistic fuzzy logic (IFL) is applied to optimize the LIM. (ii) A SOB recommendation strategy is applied to recommend the optimal learner cliques for active learners by simulating the influence propagation among learners. Influence propagation means that a learner can move towards active learners, and such behaviors can stimulate the moving behaviors of his/her neighbors. This SOB recommendation approach achieves a stable structure based on distributed and bottom-up behaviors of individuals. (iii) SPM is applied to decide the final learning objects (LOs) and navigational paths based on the recommended learner cliques. The experimental results demonstrate that $SI-IFL$SI-IFL can provide personalized and diversified recommendations, and it shows promising efficiency and adaptability in e-learning scenarios.
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