An efficient e-learning recommendation system for user preferences using hybrid optimization algorithm

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.