Recently, different recommendation techniques in e-learning have been designed that are helpful to both the learners and the educators in a wide variety of e-learning systems. Customized learning, which requires e-learning systems designed based on educational experience that suit the interests, goals, abilities, and willingness of both the learners and the educators, is required in some situations. In this research, we develop an intelligent recommender using split and conquer strategy-based clustering that can adapt automatically to the requirements, interests, and levels of knowledge of the learners. The recommender analyzes and learns the styles and characteristics of learners automatically. The different styles of learning are processed through the split and conquer strategy-based clustering. The proposed cluster-based linear pattern mining algorithm is applied to extract the functional patterns of the learners. Then, the system provides intelligent recommendations by evaluating the ratings of frequent sequences. Experiments were conducted on different groups of learners and datasets, and the proposed model suggested essential learning activities to learners based on their style of learning, interest classification, and talent features. It was experimentally found that the proposed cluster-based recommender improves the recommendation performance by resulting in more lessons completed when compared to learners present in the no-recommender cluster category. It was found that more than 65% of the learners considered all criteria to evaluate the proposed recommender. The simulation of the proposed recommender showed that for learner size values of <1000, better metric values were produced. When the learner size exceeded 1000, significant differences were obtained in the evaluated metrics. The significant differences were analyzed in terms of a computational structure depending on the recommendation list size, and the attributes of learners. The learners were also satisfied with the accuracy and speed of the recommender. For the sample dataset considered, a significant difference was observed in the standard deviation σ and mean μ of parameters, in terms of the Recall (List, User) and Ranking Score (User) measures, compared to other methods. The devised method performed well concerning all the considered metrics when compared to other methods. The simulation results signify that this recommender minimized the mean absolute error metric for the different clusters in comparison with some well-known methods.

中文翻译：

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基于集群的电子学习智能混合推荐系统的设计与分析

最近，已经设计了不同的电子学习推荐技术，这些技术对各种各样的电子学习系统中的学习者和教育者都有帮助。在某些情况下，需要进行定制化学习，这需要基于教育经验设计的电子学习系统，该系统应适合学习者和教育者的兴趣，目标，能力和意愿。在这项研究中，我们使用基于拆分和征服策略的聚类开发了一个智能推荐器，该推荐器可以自动适应学习者的需求，兴趣和知识水平。推荐者自动分析并学习学习者的风格和特征。通过基于分裂和征服策略的聚类来处理不同类型的学习。提出的基于聚类的线性模式挖掘算法被应用于提取学习者的功能模式。然后，系统通过评估频繁序列的等级来提供智能建议。针对不同组的学习者和数据集进行了实验，提出的模型根据学习者的学习风格，兴趣分类和才能特征向他们建议了必要的学习活动。通过实验发现，与无推荐群集类别中的学习者相比，基于群集的推荐者通过完成更多的课程来提高推荐性能。结果发现，超过65％的学习者考虑了所有标准来评估建议的推荐者。建议的推荐者的模拟显示，对于<1000的学习者大小值，可以产生更好的度量值。当学习者人数超过1000时，评估的指标将获得显着差异。根据推荐列表的大小和学习者的属性，在计算结构方面分析了显着差异。学习者也对推荐器的准确性和速度感到满意。对于所考虑的样本数据集，在参数的标准偏差σ和均值μ方面观察到了显着差异。根据推荐列表的大小和学习者的属性，在计算结构方面分析了显着差异。学习者也对推荐器的准确性和速度感到满意。对于所考虑的样本数据集，在参数的标准偏差σ和均值μ方面观察到了显着差异。根据推荐列表的大小和学习者的属性，在计算结构方面分析了显着差异。学习者也对推荐器的准确性和速度感到满意。对于所考虑的样本数据集，在参数的标准偏差σ和均值μ方面观察到了显着差异。与其他方法相比，召回率（列表，用户）和排名分数（用户）度量。与其他方法相比，该方法在所有考虑的指标方面表现良好。仿真结果表明，与一些众所周知的方法相比，该推荐器将不同群集的平均绝对误差度量最小化。