Abstract
Recommender systems have become an effective approach to address the difficulties users face when interacting with online services, due to the vast amounts of data available. These systems provide customised content tailored to the individual preferences of each user.
While the literature introduces various new models of recommenders, there is a growing area of research focused on additional factors that can improve recommendation quality. One such factor is data augmentation, a pre-processing technique that can influence overall performance.
The aim of this paper is to present data augmentation techniques based on data popularity measures, and to compare them with baseline and advanced solutions. Furthermore, it analyses the influence of input data size on effectiveness and utility of the augmentation process.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.73
Improving session-based recommender systems using popularity-based data augmentation
Recommender systems have become an effective approach to address the difficulties users face when interacting with online services, due to the vast amounts of data available. These systems provide customised content tailored to the individual preferences of each user.
While the literature introduces various new models of recommenders, there is a growing area of research focused on additional factors that can improve recommendation quality. One such factor is data augmentation, a pre-processing technique that can influence overall performance.
The aim of this paper is to present data augmentation techniques based on data popularity measures, and to compare them with baseline and advanced solutions. Furthermore, it analyses the influence of input data size on effectiveness and utility of the augmentation process.
Recommended Citation
Kużelewska, U.(2026). Improving session-based recommender systems using popularity-based data augmentation. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.73