Future of Information and Communication Conference (FICC) 2023
2-3 March 2023
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0131009
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.
Abstract: Online stores and service providers rely heavily on recommendation software to guide users through the vast number of available products. Consequently, the field of recommender systems has attracted increased attention from the industry and academia alike, but despite this joint effort, the field still faces several challenges. For instance, most existing work models the recommendation problem as a matrix completion problem to predict the user preference for an item. This abstraction prevents the system from utilizing the rich information from the ordered sequence of user actions logged in online sessions. To address this limitation, researchers have recently developed a promising new breed of algorithms called sequence-aware recommender systems to predict the user’s next action by utilizing the time series composed of the sequence of actions in an ongoing user session. This paper proposes a novel sequence-aware recommen-dation approach based on a complex network generated by the hidden metric space model, which combines node similarity and popularity to generate links. We build a network model from data and then use it to predict the user’s subsequent actions. The network model provides an additional information source that improves the recommendations’ accuracy. The proposed method is implemented and tested experimentally on a large dataset. The results prove that the proposed approach performs better than state-of-the-art recommendation methods.
Abdullah Alhadlaq, Said Kerrache and Hatim Aboalsamh, “A Sequence-Aware Recommendation Method based on Complex Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131009
@article{Alhadlaq2022,
title = {A Sequence-Aware Recommendation Method based on Complex Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131009},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131009},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Abdullah Alhadlaq and Said Kerrache and Hatim Aboalsamh}
}