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 …
Personalized re-ranking for recommendation
Ranking is a core task in recommender systems, which aims at providing an ordered list of
items to users. Typically, a ranking function is learned from the labeled dataset to optimize …
items to users. Typically, a ranking function is learned from the labeled dataset to optimize …
Graph convolution machine for context-aware recommender system
The latest advance in recommendation shows that better user and item representations can
be learned via performing graph convolutions on the user-item interaction graph. However …
be learned via performing graph convolutions on the user-item interaction graph. However …
Alleviating cold-start problem in CTR prediction with a variational embedding learning framework
We propose a general Variational Embedding Learning Framework (VELF) for alleviating
the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via …
the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via …
Gmcm: Graph-based micro-behavior conversion model for post-click conversion rate estimation
Purchase-related micro-behaviors, eg, favorite, add to cart, read reviews, etc., provide
implicit feedback of users' decision-making process. Such informative feedback can lead to …
implicit feedback of users' decision-making process. Such informative feedback can lead to …
FLEN: leveraging field for scalable CTR prediction
Click-Through Rate (CTR) prediction has been an indispensable component for many
industrial applications, such as recommendation systems and online advertising. CTR …
industrial applications, such as recommendation systems and online advertising. CTR …
RLNF: reinforcement learning based noise filtering for click-through rate prediction
Click-through rate (CTR) prediction aims to recall the advertisements that users are
interested in and to lead users to click, which is of critical importance for a variety of online …
interested in and to lead users to click, which is of critical importance for a variety of online …
MCGM: A multi-channel CTR model with hierarchical gated mechanism for precision marketing
Z Jiang, L Li, D Wang - World Wide Web, 2023 - Springer
Intelligent finance is a new form of business with deep integration of artificial intelligence
technology and financial industry. An important application of intelligent finance is the …
technology and financial industry. An important application of intelligent finance is the …
CFF: combining interactive features and user interest features for click-through rate prediction
L Zhang, F Liu, H Wu, X Zhuang, Y Yan - The Journal of Supercomputing, 2024 - Springer
Click-through rate is a central issue in ad recommendation and has recently received
extensive research attention in academia and industry. Research shows that the accuracy of …
extensive research attention in academia and industry. Research shows that the accuracy of …
Field-embedded factorization machines for click-through rate prediction
H Pande - arXiv preprint arXiv:2009.09931, 2020 - arxiv.org
Click-through rate (CTR) prediction models are common in many online applications such
as digital advertising and recommender systems. Field-Aware Factorization Machine (FFM) …
as digital advertising and recommender systems. Field-Aware Factorization Machine (FFM) …