To Design Most Related & Recurrent Recommendation Algorithm for Relevant Information Retrieval with Content Rating Predations Accuracy for Similar Content E-learner

Abstract

Nowadays, E-learning is progressing in effective manner that improves the quality of education system in smarter way. Here, there is no age and time constraint for user to learn from web. the most of the widely recognized methods are utilized specific classification for learning content predictions. However, there are some difficulties still available to indentify, which content are more accurate and relevant to my query. There are some existing e-learning mechanisms suggested based on their personalization of learning content. However, it makes the entire learning process as complex and time consumption. to overcome these above issues, Most Related & Recurrent Recommendation (MRRR) technique is proposed for query analysis and learning content recommendation to expose the e-learning in better way. Proposed framework is arranged the content in interactive way with more graphical representation for high visualization. Here, content recommendation can be easily predicted with high accuracy for similar content learner. The techniques offer secure access and provisioning mechanism to retrieve most relevant content with minimal time. the paper main goal is to visualize learning content based on their prediction of content ranking for similar content. once, query is processed by user, the method parses the query into attributes to retrieve the most relevant information in effective way. The technique establishes semantic relationship between the properties and meta-information to gather information about visitor of particular learning content. the rating prediction is computed for learning content based previous user satisfaction report. Based on experimental evaluation, proposed techniques improved the content visualizations success rate and minimized the query retrieval time with accuracy content rating predations.