(2020). Enhancing Collaborative Filtering Recommendations for Web-based Learning Platforms with Genetic Algorithms. In 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA (pp. 1–6). https://doi.org/10.1109/SMAP49528.2020.9248472
Περίληψη
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.
- DOI
- 10.1109/SMAP49528.2020.9248472
- ISBN
- 9781728159195
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2020
Σύνδεσμοι
BibTeX
@inproceedings{nikiforos2020enhancing,
title = {Enhancing Collaborative Filtering Recommendations for Web-based Learning Platforms with Genetic Algorithms},
author = {Maria Nefeli Nikiforos and Maria Malakopoulou and Andreana Stylidou and Aikaterini-Georgia Alvanou and Vasileios Karyotis and Panos Kourouthanassis},
url = {https://doi.org/10.1109/SMAP49528.2020.9248472},
doi = {10.1109/SMAP49528.2020.9248472},
isbn = {9781728159195},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA},
pages = {1--6},
organization = {IEEE},
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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}