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cs.IR2024
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…
cs.IR2024
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…