Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature …
Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the …
A news-recommendation system is designed to deal with massive amounts of news and provide personalized recommendations for users. Accurately modeling of news and users is …
Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to leverage meaningful data from a related domain to help CTR prediction in target domain …
X Weng, H Song, Y Lin, X Zhang, B Liu, Y Wu… - Information Sciences, 2024 - Elsevier
Tabular data are widely used for prediction tasks, but they often suffer from the curse of dimensionality and noise, leading to degradation in the performance and robustness of …
X Tao, M Ha, Q Ma, H Cheng, W Lin, X Guo… - Proceedings of the 17th …, 2023 - dl.acm.org
In online recommendation, financial service, etc., the most common application of multi-task learning (MTL) is the multi-step conversion estimations. A core property of the multi-step …
J Ye, S Liu, C Qiu, Z Zhang - Information Fusion, 2024 - Elsevier
Images captured in challenging conditions often suffer from the co-existence of low contrast and low resolution. However, most joint enhancement methods focus on fitting a direct …
K Xiao, X Jiang, P Hou, H Zhu - Information Processing & Management, 2024 - Elsevier
Deep models have shown the effectiveness in various areas, eg, finance, healthcare and recommendation system. Among them, default prediction is a major application in the …
Click-through rate (CTR) prediction has become increasingly indispensable for various Internet applications. Traditional CTR models convert the multi-field categorical data into ID …