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
Most implemented papers
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt, Deep Session Interest Network(DSIN)
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigabytes in large-scale settings.
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
shenweichen/DeepCTR
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RecSys 2020
Moreover, through extensive experiments across SOTA MTL models, we have observed an interesting seesaw phenomenon that performance of one task is often improved by hurting the performance of some other tasks.
A Dual Input-aware Factorization Machine for CTR Prediction
shenweichen/DeepCTR
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IJCAI 2020
Factorization Machines (FMs) refer to a class of general predictors working with real valued feature vectors, which are well-known for their ability to estimate model parameters under significant sparsity and have found successful applications in many areas such as the click-through rate (CTR) prediction.
Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
wnzhang/deep-ctr
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Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known.
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
The data involved in CTR prediction are typically multi-field categorical data, i. e., every feature is categorical and belongs to one and only one field.
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
The key of this task is to model feature interactions among different feature fields.
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
reczoo/BARS
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We have publicly released the benchmarking code, evaluation protocols, and hyper-parameter settings of our work to promote reproducible research in this field.
DKN: Deep Knowledge-Aware Network for News Recommendation
To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.
