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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Click-Through Rate Prediction models and implementations
MovieLens
CBT
Criteo
iPinYou
TripClick
KDD12
MerRec
Most implemented papers
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models.
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.
Field-Embedded Factorization Machines for Click-through rate prediction
Field-Aware Factorization Machine (FFM) and Field-weighted Factorization Machine (FwFM) are state-of-the-art among the shallow models for CTR prediction.
FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
recommendation-algorithm/fibinet
•
•
Click-Through Rate (CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed.
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
reczoo/RecZoo
•
•
As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.
Field-aware factorization machines for CTR prediction
microsoft/recommenders
•
•
RecSys 2016
Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task.
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
Inspired by these observations, we propose a novel model named GateNet which introduces either the feature embedding gate or the hidden gate to the embedding layer or hidden layers of DNN CTR models, respectively.
ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
In this paper, We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions by dynamically refining each feature's embedding according to the input context.
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
anonctr/GDCN
•
•
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.
