Hybrid Recommendation System with Enhanced Generalized Sequential Pattern Algorithm for ELearning System

Recommendation system have been established crucial role in the different applications such as online applications, e-learning services, social networks and etc. The traditional recommender systems have recorded significant success in e-learning domain, they still experience drawbacks in making accurate recommendations of learning resources in differences of learner characteristics such as learning style, knowledge level as well as learners sequential learning patterns. Most of the existing recommendation techniques do not consider differences in learner characteristics. In this research paper, hybrid recommendation system based an efficient sequential pattern mining of Enhanced Generalized Sequential Pattern (EGSP) Algorithm is proposed for recommend the student performance. The proposed recommendation approach helps to extract the more relevant student performance related features from the input data. After that, calculate the similarity based on the features and making predictions for the target learner by cosine metric. Next, generation of top N learning items by the collaborative filtering recommendation engine. At last, application of the EGSP algorithm to the top N learning features to generate the final recommendations for the target learner. An experimental analysis conducted on Learning Management System (LMS) dataset and the performance was measured using an evaluation metrics such as precision, recall, and f-measure. Compared to the GSP-Collaborative Filtering-Content based Approach, the proposed EGSP algorithm achieved 4% of improved recommendation result.