Exploiting Spiking Neural Networks for Click-Through Rate Prediction in Personalized Online Advertising Systems

Albin Uruqi and Iosif Viktoratos ()
Additional contact information
Albin Uruqi: Department of Computer Science, ACT College, Thessaloniki, Vasiliou Sevenidi 17, 555 35 Pilea, Greece
Iosif Viktoratos: Department of Computer Science, ACT College, Thessaloniki, Vasiliou Sevenidi 17, 555 35 Pilea, Greece

Forecasting, 2025, vol. 7, issue 3, 1-17

Abstract: This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and energy efficiency of SNNs, combined with attention-based mechanisms, the TRA–SNN model captures temporal dynamics and rate-based patterns to achieve performance comparable to state-of-the-art Artificial Neural Network (ANN)-based models, such as Deep & Cross Network v2 (DCN-V2) and FinalMLP. The models were trained and evaluated on the Avazu and Digix datasets, using standard metrics like AUC-ROC and accuracy. Through rigorous hyperparameter tuning and standardized preprocessing, this study ensures fair comparisons across models, highlighting SNNs’ potential for scalable, sustainable deployment in resource-constrained environments like mobile devices and large-scale ad platforms. This work is the first to apply SNNs to CTR prediction, setting a new benchmark for energy-efficient predictive modeling and opening avenues for future research in hybrid SNN–ANN architectures across domains like finance, healthcare, and autonomous systems.

Keywords: artificial intelligence; machine learning; click-through rate (ctr); artificial neural networks; spiking neural networks (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-9394/7/3/38/pdf (application/pdf)
https://www.mdpi.com/2571-9394/7/3/38/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:7:y:2025:i:3:p:38-:d:1704996

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().