A Hybrid Recommender System Guided by Semantic User Profiles for Search in the E-learning Domain

Abstract

Various concepts, methods, and technical architectures of recommender systems have been integrated into E-commerce storefronts, such as Amazon.com, Netflix, etc. Thereby, recently, Web users have become more familiar with the notion of recommendations. Nevertheless, little work has been done to integrate recommender systems into scientific information retrieval repositories, such as libraries, content management systems, online learning platforms, etc. This paper presents an implementation of a hybrid recommender system to personal the user's experience on a real online learning repository and vertical search engine named HyperManyMedia. This repository contains educational content of courses, lectures, multimedia resources, etc. The main objective of this paper is to illustrate the methods, concepts, and architecture that we used to integrate a hybrid recommender system into the HyperManyMedia repository. This recommender system is driven by two types of recommendations: content-based (domain ontology model) and rule-based (learner's interest- based and cluster-based). Finally, combining the content- based and the rule-based models provides the user with hybrid recommendations that influence the ranking of the retrieved documents with different weights. Our experiments were carried out on the HyperManyMedia semantic search engine at Western Kentucky University. We used Top-n-Recall and Top-n-Precision to measure the effectiveness of re-ranking based on the learner's semantic profile. Overall, the results demonstrate the effectiveness of the re-ranking based on personalization.