Click-Through Rate Prediction
133 papers with code • 19 benchmarks • 6 datasets
Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.
( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )
Libraries
Use these libraries to find Click-Through Rate Prediction models and implementationsLatest papers
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples.
Discrete Semantic Tokenization for Deep CTR Prediction
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.
Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization
We explore the typical data characteristics and optimization statistics of CTR prediction, revealing a strong positive correlation between the top hessian eigenvalue and feature frequency.
Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
We conduct a theoretical analysis of the learning process for the weights in the linear component, revealing how group-wise properties of training data influence them.
A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.
CETN: Contrast-enhanced Through Network for CTR Prediction
Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search.
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
To address the above issue, we propose the Universal Feature Interaction Network (UFIN) approach for CTR prediction.
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
It is crucial to effectively model feature interactions to improve the prediction performance of CTR models.
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
In addition, we present a new architecture of assigning independent FR modules to separate sub-networks for parallel CTR models, as opposed to the conventional method of inserting a shared FR module on top of the embedding layer.
RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction
Click-through rate (CTR) prediction is a critical task in recommendation systems, serving as the ultimate filtering step to sort items for a user.