A Hybrid E-learning Recommendation Approach Based on Learners’ Influence Propagation

A Hybrid E-learning Recommendation Approach Based on Learners’ Influence Propagation


In e-learning recommender systems, interpersonal information between learners is very scarce, which makes it difficult to apply collaborative filtering (CF) techniques. In this study, we propose a hybrid filtering (HF) recommendation approach (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. 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 toward active learners, and such behaviors can stimulate the moving behaviors of his 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 can provide personalized and diversified recommendations, and it shows promising efficiency and adaptability in e-learning scenarios.



  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.


  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL


Shanshan Wan, Zhendong Niu, “A Hybrid E-learning Recommendation Approach Based on Learners’ Influence Propagation”, IEEE Transactions on Knowledge and Data Engineering, 2019.

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