WEBLORS – A Personalized Web-Based Recommender System

Mohammad Belghis-Zadeh, Hazra Imran, Maiga Chang, Sabine Graf

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

1 Citation (Scopus)


Nowadays, personalization and adaptivity becomes more and more important in most systems. When it comes to education and learning, personalization can provide learners with better learning experiences by considering their needs and characteristics when presenting them with learning materials within courses in learning management systems. One way to provide students with more personal learning materials is to deliver personalized content from the web. However, due to information overload, finding relevant and personalized materials from the web remains a challenging task. This paper presents an adaptive recommender system called WEBLORS that aims at helping learners to overcome the information overload by providing them with additional personalized learning materials from the web to increase their learning and performance. This paper also presents the evaluation of WEBLORS based on its recommender system acceptance using data from 36 participants. The evaluation showed that overall, participants had a positive experience interacting with WEBLORS. They trusted the recommendations and found them helpful to improve learning and performance, and they agreed that they would like to use the system again.

Original languageEnglish
Title of host publicationAdvances in Web-Based Learning – ICWL 2019 - 18th International Conference, 2019, Proceedings
EditorsMichael A. Herzog, Zuzana Kubincová, Peng Han, Marco Temperini
Number of pages9
Publication statusPublished - 2019
Event18th International Conference on Advances in Web-Based Learning, ICWL 2019 - Magdeburg, Germany
Duration: 23 Sep. 201925 Sep. 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11841 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Advances in Web-Based Learning, ICWL 2019


  • Personalization
  • Recommender systems
  • Web mining


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