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.

中文翻译：

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一种基于学习者影响力传播的混合电子学习推荐方法

在电子学习推荐系统中，学习者之间的人际信息非常稀缺，这使得应用协同过滤（CF）技术来实现推荐变得困难。在这项研究中，我们提出了一种混合过滤推荐方法（$SI-IFL$秒一世——一世F升) 将学习者影响模型 (LIM)、基于自组织 (SOB) 的推荐策略和序列模式挖掘 (SPM) 结合在一起，向学习者推荐学习对象 (LO)。该方法的工作原理如下： (i) LIM 用于通过计算学习者对他人施加的影响来获取人际信息。LIM 由学习者相似性、知识可信度和学习者聚合组成，同时，LIM 与评分无关。此外，为了解决学习者的不确定性和模糊性，直觉模糊逻辑 (IFL) 被应用于优化 LIM。(ii) 通过模拟学习者之间的影响传播，应用 SOB 推荐策略为主动学习者推荐最佳学习者团。影响传播意味着学习者可以转向主动学习者，并且这样的行为可以刺激他/她的邻居的移动行为。这种 SOB 推荐方法基于个体的分布式和自下而上的行为实现了稳定的结构。(iii) SPM 用于根据推荐的学习者集团决定最终的学习对象 (LO) 和导航路径。实验结果表明$SI-IFL$秒一世——一世F升 可以提供个性化和多样化的推荐，在电子学习场景中显示出良好的效率和适应性。