A Hybrid Personalized Tag Recommendationsfor Social E-Learning System

Collaborative filtering (CF) is one of the most popular techniques behind the success of recommendation system. It predicts the interest of users by collecting information from past users who have the same opinions. Starting recommendation methods like content based recommendation lost its import and the pattern setting collaborative filtering methodology picks up its proficiency in all fields.With a specific end goal to perform a better data recommendation cluster based collaborative filtering methodologies are used these days. Clustering leads to the reduction of huge data set into smaller data set in which all the services are similar to one another. In this paper, a hybrid personalized recommender system based on a clustering algorithm and Collaborative Filtering approach is proposed for social E-Learning systems and the technique is implemented and tested using an E-Learning environmental dataset. At long last this calculation is contrasted and slope one calculation and the execution is dissected by utilizing the measurements Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

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