All Outputs (3)

Mobile recommender systems: Identifying the major concepts (2018)
Journal Article
Pimenidis, E., Polatidis, N., & Mouratidis, H. (2019). Mobile recommender systems: Identifying the major concepts. Journal of Information Science, 45(3), 387-397. https://doi.org/10.1177/0165551518792213

© The Author(s) 2018. This article identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an informa... Read More about Mobile recommender systems: Identifying the major concepts.

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks (2018)
Journal Article
Polatidis, N., Pimenidis, E., Pavlidis, M., Papastergiou, S., & Mouratidis, H. (2020). From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks. Evolving Systems, 11(2), 479-490. https://doi.org/10.1007/s12530-018-9234-z

Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to... Read More about From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks.

Reproducibility of experiments in recommender systems evaluation (2018)
Journal Article
Polatidis, N., Kapetanakis, S., Pimenidis, E., & Kosmidis, K. (2018). Reproducibility of experiments in recommender systems evaluation. IFIP Advances in Information and Communication Technology, 519, 401-409. https://doi.org/10.1007/978-3-319-92007-8_34

© IFIP International Federation for Information Processing 2018 Published by Springer International Publishing AG 2018. All Rights Reserved. Recommender systems evaluation is usually based on predictive accuracy metrics with better scores meaning rec... Read More about Reproducibility of experiments in recommender systems evaluation.