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Click-Through Rate Prediction

26 papers with code ยท Miscellaneous

Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.

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Latest papers without code

Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction

12 Oct 2019

The key of this task is to model feature interactions among different feature fields.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction

15 Jul 2019

The focus of this paper is to identify the best combination of loss functions and models that enable large-scale learning from a continuous stream of data in the presence of delayed labels.

CLICK-THROUGH RATE PREDICTION

Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling

25 Jun 2019

In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model.

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FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

15 May 2019

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.

CLICK-THROUGH RATE PREDICTION FEATURE IMPORTANCE RECOMMENDATION SYSTEMS

Learning Representations of Categorical Feature Combinations via Self-Attention

ICLR 2019

In most current DNN based models, feature embeddings are simply concatenated for further processing by networks.

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Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings

25 Apr 2019

We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs.

CLICK-THROUGH RATE PREDICTION META-LEARNING

CPM-sensitive AUC for CTR prediction

23 Apr 2019

This is because there is a gap between offline AUC and online CPM.

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Block-distributed Gradient Boosted Trees

23 Apr 2019

As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.

CLICK-THROUGH RATE PREDICTION LEARNING-TO-RANK

Sparse Tensor Additive Regression

31 Mar 2019

Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing.

CLICK-THROUGH RATE PREDICTION

Field-aware Neural Factorization Machine for Click-Through Rate Prediction

25 Feb 2019

This model can have strong second order feature interactive learning ability like Field-aware Factorization Machine, on this basis, deep neural network is used for higher-order feature combination learning.

CLICK-THROUGH RATE PREDICTION FEATURE ENGINEERING RECOMMENDATION SYSTEMS