27 citations · 28 across the 6 of their papers we have counts for
6 papers
Polyhedral Conic Classifier for CTR Prediction
Beyza Turkmen, Ramazan Tarik Turksoy, Hasan Saribas +1
This paper introduces a novel approach for click-through rate (CTR) prediction within industrial recommender systems, addressing the inherent challenges of numerical imbalance and…
Pairwise Ranking Loss for Multi-Task Learning in Recommender Systems
Furkan Durmus, Hasan Saribas, Said Aldemir +2
Multi-Task Learning (MTL) plays a crucial role in real-world advertising applications such as recommender systems, aiming to achieve robust representations while minimizing resourc…
MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction
Hasan Saribas, Cagri Yesil, Serdarcan Dilbaz +1
With the increasing complexity and scale of click-through rate (CTR) prediction tasks in online advertising and recommendation systems, accurately estimating the importance of feat…
STEC: See-Through Transformer-based Encoder for CTR Prediction
Serdarcan Dilbaz, Hasan Saribas
Click-Through Rate (CTR) prediction holds a pivotal place in online advertising and recommender systems since CTR prediction performance directly influences the overall satisfactio…
Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces
Hakan Cevikalp, Hasan Saribas
The classification loss functions used in deep neural network classifiers can be grouped into two categories based on maximizing the margin in either Euclidean or angular spaces. E…
TRAT: Tracking by Attention Using Spatio-Temporal Features
Hasan Saribas, Hakan Cevikalp, Okan Köpüklü +1
Robust object tracking requires knowledge of tracked objects' appearance, motion and their evolution over time. Although motion provides distinctive and complementary information e…