Applying Machine Learning & Knowledge Discovery to Intelligent Agent-Based Recommendation for Online Learning Systems

E-learning is one of the favorite jobs where all the learners can learn through different online sites to update their knowledge acquisition. Though online learning supports several benefits, there are many challenges to be considered, for example, the information resources, quality of the search, accessing the correct information, getting the search results within a reasonable computing time, etc. This research develops the new natural language processing (NLP) intelligent and online recommender using the learner's profile information and semantic analysis of the search history at different times. The intelligent clustering strategy is applied, so the system learns automatically, and the learner's characteristics are analyzed periodically. The simulation results of the proposed research are compared with the state-of-the-art techniques and provide the minimized error rates to the selection of courses and recommendations with the recommendation accuracy in (98.2%, 98.9%). The MAE lies between (0.12, 0.35) for the considered clusters. Compared to the other methods, the method works well for clusters of sizes 10, 20, 30, 40, and 50.