Fuzzy Family Tree Similarity based Effective E-learning Recommender System

Recommender systems are nowadays used on a larger scale in e-learning field to populate the items of interest to the online users. These users, be it, teachers, students or researchers are in need of relevant recommendations list than a list containing mostly irrelevant or unordered recommendations. This paper aims at generating a list of recommendation alternatives with highest rankings among the anticipatory ratings of various key concepts ready to be read through by the online users. This novel approach makes use of fuzzy family tree similarity algorithm to select the key concepts that are of more interest to the online users. Empirical evaluations prove that the proposed technique is efficient and feasible in including the key concepts in the recommendation list, which would otherwise be left out in the conventional tree similarity technique. Anticipatory ratings are determined based on the recommendation alternatives, user key concept rate (UKCR) matrix and neighbors sorted in the order of semantic and content similarities.