no code implementations • 29 Aug 2023 • Serdarcan Dilbaz, Hasan Saribas
Most CTR prediction models have relied on a single fusion and interaction learning strategy.
Ranked #1 on Click-Through Rate Prediction on MovieLens 1M
no code implementations • 25 Aug 2023 • Hasan Saribas, Cagri Yesil, Serdarcan Dilbaz, Halit Orenbas
In this paper, we propose an attention-based approach that leverages max and mean pooling operations, along with a bit-wise attention mechanism, to enhance feature importance estimation in CTR prediction.
Ranked #2 on Click-Through Rate Prediction on Frappe
no code implementations • 22 Dec 2022 • Hakan Cevikalp, Hasan Saribas
This paper introduces a novel classification loss that maximizes the margin in both the Euclidean and angular spaces at the same time.
no code implementations • 18 Nov 2020 • Hasan Saribas, Hakan Cevikalp, Okan Köpüklü, Bedirhan Uzun
Although motion provides distinctive and complementary information especially for fast moving objects, most of the recent tracking architectures primarily focus on the objects' appearance information.