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 interest network for click-through rate prediction
Click-through rate prediction is an essential task in industrial applications, such as online
advertising. Recently deep learning based models have been proposed, which follow a …
advertising. Recently deep learning based models have been proposed, which follow a …
Online decision making with high-dimensional covariates
Big data have enabled decision makers to tailor decisions at the individual level in a variety
of domains, such as personalized medicine and online advertising. Doing so involves …
of domains, such as personalized medicine and online advertising. Doing so involves …
Curriculum disentangled recommendation with noisy multi-feedback
Learning disentangled representations for user intentions from multi-feedback (ie, positive
and negative feedback) can enhance the accuracy and explainability of recommendation …
and negative feedback) can enhance the accuracy and explainability of recommendation …
[PDF][PDF] Deep feedback network for recommendation
Both explicit and implicit feedbacks can reflect user opinions on items, which are essential
for learning user preferences in recommendation. However, most current recommendation …
for learning user preferences in recommendation. However, most current recommendation …
A unified framework for multi-domain ctr prediction via large language models
Multi-Domain Click-Through Rate (MDCTR) prediction is crucial for online recommendation
platforms, which involves providing personalized recommendation services to users in …
platforms, which involves providing personalized recommendation services to users in …
Hierarchical reinforcement learning for integrated recommendation
Integrated recommendation aims to jointly recommend heterogeneous items in the main
feed from different sources via multiple channels, which needs to capture user preferences …
feed from different sources via multiple channels, which needs to capture user preferences …
SAR-Net: A scenario-aware ranking network for personalized fair recommendation in hundreds of travel scenarios
The travel marketing platform of Alibaba serves an indispensable role for hundreds of
different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized …
different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized …
Towards automated neural interaction discovery for click-through rate prediction
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in
recommender systems, driving personalized experience for billions of consumers. Neural …
recommender systems, driving personalized experience for billions of consumers. Neural …
Deep ctr prediction in display advertising
Click through rate (CTR) prediction of image ads is the core task of online display
advertising systems, and logistic regression (LR) has been frequently applied as the …
advertising systems, and logistic regression (LR) has been frequently applied as the …