RT info:eu-repo/semantics/conferenceObject
T1 AdSight: Scalable and Accurate Quantification of User Attention in Multi-Slot Sponsored Search
A1 Villaizán Vallelado, Mario
A1 Salvatori, Matteo
A1 Latifzadeh, Kayhan
A1 Penta, Antonio
A1 Leiva, Luis A.
A1 Arapakis, Ioannis
K1 SERP
K1 Búsqueda patrocinada
K1 Publicidad online
K1 Seguimiento del cursor del ratón
K1 Anuncios multiespacio
K1 Visualizaciones directas
K1 Atención del usuario
K1 Redes neuronales
AB Modern Search Engine Results Pages (SERPs) present complex layouts where multiple elements compete for visibility. Attention modelling is crucial for optimising web design and computational advertising, whereas attention metrics can inform ad placement and revenue strategies. We introduce AdSight, a method leveraging mouse cursor trajectories to quantify in a scalable and accurate manner user attention in multi-slot environments like SERPs. AdSight uses a novel Transformer-based sequence-to-sequence architecture where the encoder processes cursor trajectory embeddings, and the decoder incorporates slot-specific features, enabling robust attention prediction across various SERP layouts. We evaluate our approach on two Machine Learning tasks: (1)~regression, to predict fixation times and counts; and (2)~classification, to determine some slot types were noticed. Our findings demonstrate the model's ability to predict attention with unprecedented precision, offering actionable insights for researchers and practitioners.
PB Association for Computing Machinery, Inc
SN 9798400715921
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/78833
UL https://uvadoc.uva.es/handle/10324/78833
LA eng
NO Nicola Ferro, Maria Maistro, Gabriella Pasi, Omar Alonso, Andrew Trotman, Suzan Verberne. SIGIR '25: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. Padua, 2025, p. 255-265.
NO Producción Científica
DS UVaDOC
RD 11-nov-2025