3 papers
cs.IR2024
Effects of Using Synthetic Data on Deep Recommender Models' Performance
Fatih Cihan Taskin, Ilknur Akcay, Muhammed Pesen +3
Recommender systems are essential for enhancing user experiences by suggesting items based on individual preferences. However, these systems frequently face the challenge of data i…
cs.IR2024
Mutual Learning for Finetuning Click-Through Rate Prediction Models
Ibrahim Can Yilmaz, Said Aldemir
Click-Through Rate (CTR) prediction has become an essential task in digital industries, such as digital advertising or online shopping. Many deep learning-based methods have been i…
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…