The expanding approval of e-learning structure has made the need for the customized suggestion prototype which can be utilized to advance the successful learning condition for the learners. Customized suggestion model is a particular sort of data separating framework used to recognize a lot of articles that are applicable to a e-learners. In this paper, we mainly propose the efficient e-learning recommendation (EELR) system for user preferences using hybrid optimization algorithm (HOA). EELR system constructs a HOA with deep recurrent neural network (DRNN) and improved whale optimization (IWO) algorithm. First, DRNN is utilized to order the e-learner types dependent on these e-learner gatherings, clients can acquire course proposal from the gathering's persuasion. Thereafter, the conduct and the inclinations of the learners are examined via completing the mining of the arrangements watched every now and again by the IWO calculation. Rather than a learner effectively looking for data, recommender frameworks give counsel to students about articles they may wish to analyze. At last, the proposal of the e-learning depends on the appraisals comparing to these arrangements watched often. This proposed system is going to implement and validate in numerous e-learning entries against the client inclinations over some undefined time frame and demonstrated to be more proficiency and exactness contrasted with the customary recommender framework. This strategy can help learners to grasp the knowledge system and learning direction, and improve their learning efficiency. Observation results show that the proposed methodology empowers the asset suggestion to singular clients, which is started from different sources.

电子学习结构的扩大认可使得需要定制的建议原型可以用来促进学习者的成功学习条件。定制化建议模型是一种特殊的数据分离框架，用于识别大量适用于电子学习者的文章。在本文中，我们主要使用混合优化算法（HOA）提出针对用户偏好的高效电子学习推荐（EELR）系统。EELR 系统使用深度循环神经网络 (DRNN) 和改进的鲸鱼优化 (IWO) 算法构建 HOA。首先，利用DRNN根据这些e-learner聚会对e-learner类型进行排序，客户可以从聚会的说服中获取课程建议。此后，学习者的行为和倾向是通过IWO计算完成对不时观察的安排的挖掘来检查的。推荐框架不是让学习者有效地寻找数据，而是就他们可能希望分析的文章向学生提供建议。最后，与经常观看的这些安排相比，在线学习的建议取决于评估。这个提议的系统将在一些未定义的时间范围内针对客户的倾向在众多电子学习条目中实施和验证，并证明与传统推荐框架相比更加熟练和准确。这种策略可以帮助学习者掌握知识体系和学习方向，提高学习效率。