2020
Nikiforos, M. N.; Malakopoulou, M.; Stylidou, A.; Alvanou, A. -G.; Karyotis, V.; Kourouthanassis, P.
Institute of Electrical and Electronics Engineers Inc., 2020, ISBN: 9781728159195, (cited By 3; Conference of 15th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2020 ; Conference Date: 29 October 2020 Through 30 October 2020; Conference Code:164932).
Abstract | Links | BibTeX | Tags: Collaborative filtering recommendations; Generic architecture; On-line education; Online course; Potential customers; Potential impacts; Web based learning; Web-based learning platforms, Collaborative filtering; Computer aided instruction; Curricula; Genetic algorithms; Online systems; Semantics; Social networking (online); Websites, E-learning
@conference{Nikiforos2020,
title = {Enhancing Collaborative Filtering Recommendations for Web-based Learning Platforms with Genetic Algorithms},
author = {M. N. Nikiforos and M. Malakopoulou and A. Stylidou and A. -G. Alvanou and V. Karyotis and P. Kourouthanassis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097598439&doi=10.1109%2fSMAP49528.2020.9248472&partnerID=40&md5=e8ab00020bbab229e12e04a952b494b2},
doi = {10.1109/SMAP49528.2020.9248472},
isbn = {9781728159195},
year = {2020},
date = {2020-01-01},
journal = {SMAP 2020 - 15th International Workshop on Semantic and Social Media Adaptation and Personalization},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Web-based learning platforms are now offering a considerable amount of training options, exhibiting great variability, similar to the one encountered by a potential customer in electronic shops. To mitigate this, efficient and effective recommendations engines are needed, capable of satisfying the specific needs of each user, while achieving the best possible promotion of available onlinetraining. This paper discusses the design of a potential generic architecture for online education recommender systems, specifically targeted for promoting online courses and web-based learning material. From an algorithmic perspective, the system relies on item-based and user-based collaborativefiltering approaches. It extends this approach with a genetic algorithm, thus increasing its potential impact. Overall, the paper paves the ground for the specification of generic principles governing the design of personalized online education platforms as well as identifying metrics for evaluating their performance. © 2020 IEEE.},
note = {cited By 3; Conference of 15th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2020 ; Conference Date: 29 October 2020 Through 30 October 2020; Conference Code:164932},
keywords = {Collaborative filtering recommendations; Generic architecture; On-line education; Online course; Potential customers; Potential impacts; Web based learning; Web-based learning platforms, Collaborative filtering; Computer aided instruction; Curricula; Genetic algorithms; Online systems; Semantics; Social networking (online); Websites, E-learning},
pubstate = {published},
tppubtype = {conference}
}
Web-based learning platforms are now offering a considerable amount of training options, exhibiting great variability, similar to the one encountered by a potential customer in electronic shops. To mitigate this, efficient and effective recommendations engines are needed, capable of satisfying the specific needs of each user, while achieving the best possible promotion of available onlinetraining. This paper discusses the design of a potential generic architecture for online education recommender systems, specifically targeted for promoting online courses and web-based learning material. From an algorithmic perspective, the system relies on item-based and user-based collaborativefiltering approaches. It extends this approach with a genetic algorithm, thus increasing its potential impact. Overall, the paper paves the ground for the specification of generic principles governing the design of personalized online education platforms as well as identifying metrics for evaluating their performance. © 2020 IEEE.