Click-through rate prediction in online advertising: A literature review
Y Yang, P Zhai - Information Processing & Management, 2022 - Elsevier
Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …
prevalent issue in online advertising, attracting much research attention in the past decades …
Deep learning for click-through rate estimation
Click-through rate (CTR) estimation plays as a core function module in various personalized
online services, including online advertising, recommender systems, and web search etc …
online services, including online advertising, recommender systems, and web search etc …
Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction
Learning feature interactions is crucial for click-through rate (CTR) prediction in
recommender systems. In most existing deep learning models, feature interactions are either …
recommender systems. In most existing deep learning models, feature interactions are either …
Bars: Towards open benchmarking for recommender systems
The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …
recommendation techniques. Despite the significant progress made in both research and …
Open benchmarking for click-through rate prediction
Click-through rate (CTR) prediction is a critical task for many applications, as its accuracy
has a direct impact on user experience and platform revenue. In recent years, CTR …
has a direct impact on user experience and platform revenue. In recent years, CTR …
Multi-graph convolution collaborative filtering
Personalized recommendation is ubiquitous, playing an important role in many online
services. Substantial research has been dedicated to learning vector representations of …
services. Substantial research has been dedicated to learning vector representations of …
Map: A model-agnostic pretraining framework for click-through rate prediction
With the widespread application of online advertising systems, click-through rate (CTR)
prediction has received more and more attention and research. The most prominent features …
prediction has received more and more attention and research. The most prominent features …
An embedding learning framework for numerical features in ctr prediction
Click-Through Rate (CTR) prediction is critical for industrial recommender systems, where
most deep CTR models follow an Embedding & Feature Interaction paradigm. However, the …
most deep CTR models follow an Embedding & Feature Interaction paradigm. However, the …
Cl4ctr: A contrastive learning framework for ctr prediction
Many Click-Through Rate (CTR) prediction works focused on designing advanced
architectures to model complex feature interactions but neglected the importance of feature …
architectures to model complex feature interactions but neglected the importance of feature …
Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment …
Visual or manual characterization and classification of atherosclerotic plaque lesions are
tedious, error-prone, and time-consuming. The purpose of this study is to develop and …
tedious, error-prone, and time-consuming. The purpose of this study is to develop and …