Title: A multi-criteria course recommendation system based on the skyline BNL and top-kws algorithms

Authors: Aicha Er-Rafyg; Abdellah Idrissi; Kaoutar El Handri

Addresses: IPSS Team, Computer Science Department, Faculty of Science, Mohammed V University in Rabat, Rabat, Morocco ' IPSS Team, Computer Science Department, Faculty of Science, Mohammed V University in Rabat, Rabat, Morocco ' Information Science and Intelligent Embedded Systems Team, Laboratory of Innovation in Management and Engineering for the Enterprise (LIMIE), Higher Institute of Business and Engineering, ISGA Casablanca, Casablanca, Morocco

Abstract: In today's digital age, online courses have become a valuable tool for learners to acquire new skills and knowledge. The global outbreak of COVID-19 has further accelerated the adoption of online learning as education service providers are forced to move their courses online to ensure the continuity of education. However, with many online courses, learners often find it challenging to select courses that meet their preferences and requirements. To address this issue, recommender systems (RS) have emerged as a popular solution for automatically analysing data and providing personalised recommendations to learners. Our previous work proposed a course RS based on the skyline block-nested-loops (BNL) algorithm. This algorithm filters courses based on multiple criteria, such as course duration, price, difficulty level, and rating, allowing learners to select courses that meet their specific preferences. However, the skyline BNL algorithm has limitations when learners must consider several criteria simultaneously. This limitation led us to propose a new RS that combines skyline BNL and top-k weighted sum (top-kws). The top-kws algorithm ranks courses based on a weighted sum of their features, allowing learners to select courses based on their relative importance.

Keywords: multi-criteria recommender systems; courses recommendation system; skyline BNL algorithm; top-kws algorithm; e-learning.

DOI: 10.1504/IJLT.2025.145212

International Journal of Learning Technology, 2025 Vol.20 No.1, pp.64 - 85

Received: 19 May 2023
Accepted: 19 Sep 2023
Published online: 26 Mar 2025 *

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