Click-Through Rate Prediction
135 papers with code • 19 benchmarks • 7 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 )
Benchmarks
These leaderboards are used to track progress in Click-Through Rate Prediction
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Libraries
Use these libraries to find Click-Through Rate Prediction models and implementations
MovieLens
CBT
Criteo
iPinYou
TripClick
KDD12
MerRec
Latest papers
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions.
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
skylerlinn/understanding-the-ranking-loss
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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.
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems
mercari/mercari-ml-merrec-pub-us
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In the evolving e-commerce field, recommendation systems crucially shape user experience and engagement.
Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
mitao-cat/feature-level_bias
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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
salmon1802/cetn
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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
rucaibox/ufin
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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
xue-pai/FuxiCTR
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Proceedings of the 32nd ACM International Conference on Information and Knowledge Management 2023
It is crucial to effectively model feature interactions to improve the prediction performance of CTR models.
