Click-Through Rate Prediction
120 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 )
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
Most implemented papers
Wide & Deep Learning for Recommender Systems
Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions.
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
xue-pai/FuxiCTR
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Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.
Deep Interest Network for Click-Through Rate Prediction
zhougr1993/DeepInterestNetwork
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In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.
Deep & Cross Network for Ad Click Predictions
Feature engineering has been the key to the success of many prediction models.
Deep Interest Evolution Network for Click-Through Rate Prediction
mouna99/dien
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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
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
DeepGraphLearning/RecommenderSystems
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Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features.
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
