TY - GEN
T1 - Adaptive Noise Injection in Variational Autoencoders for Enhancing Fairness in Group Recommendations
AU - Ahmad, Emaz Uddin
AU - Stratigi, Maria
AU - Stefanidis, Kostas
N1 - Publisher Copyright:
© 2026 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2026
Y1 - 2026
N2 - This paper proposes an enhanced Variational Autoencoder (VAE) based framework that introduces adaptive noise injection into the latent space to promote fairness and satisfaction in group recommendations. In contrast to conventional VAEs that depend on static Gaussian noise, the proposed model dynamically learns data-dependent, adaptive noise from user representations, enhancing its ability to predict uncertainty and reducing bias towards dominant user preferences. The framework is further enhanced through Bayesian optimization, which is employed to fine-tune the hyperparameters of the Variational Autoencoder. Comprehensive experiments on the MovieLens 10M dataset across homogeneous, heterogeneous, and mixed groups demonstrate that the adaptive noise VAE model consistently outperforms the static noise baseline.
AB - This paper proposes an enhanced Variational Autoencoder (VAE) based framework that introduces adaptive noise injection into the latent space to promote fairness and satisfaction in group recommendations. In contrast to conventional VAEs that depend on static Gaussian noise, the proposed model dynamically learns data-dependent, adaptive noise from user representations, enhancing its ability to predict uncertainty and reducing bias towards dominant user preferences. The framework is further enhanced through Bayesian optimization, which is employed to fine-tune the hyperparameters of the Variational Autoencoder. Comprehensive experiments on the MovieLens 10M dataset across homogeneous, heterogeneous, and mixed groups demonstrate that the adaptive noise VAE model consistently outperforms the static noise baseline.
KW - Fairness
KW - Group Recommendations
KW - Recommender Systems
KW - Variational Autoencoder (VAE)
UR - https://www.scopus.com/pages/publications/105037453530
M3 - Conference contribution
AN - SCOPUS:105037453530
VL - 4186
T3 - CEUR Workshop Proceedings
SP - 93
EP - 99
BT - DOLAP 2026 Design, Optimization, Languages and Analytical Processing of Big Data 2026
PB - CEUR-WS
T2 - International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data
Y2 - 24 March 2026 through 24 March 2026
ER -