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Click-Through Rate Prediction

27 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

DeepEnFM: Deep neural networks with Encoder enhanced Factorization Machine

ICLR 2020

Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings with the clues of other fields.

CLICK-THROUGH RATE PREDICTION

FLEN: Leveraging Field for Scalable CTR Prediction

12 Nov 2019

By suitably exploiting field information, the field-wise bi-interaction pooling captures both inter-field and intra-field feature conjunctions with a small number of model parameters and an acceptable time complexity for industrial applications.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction

3 Nov 2019

In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.

CLICK-THROUGH RATE PREDICTION

Conversion Rate Prediction via Post-Click Behaviour Modeling

15 Oct 2019

After grouping deterministic actions together, we construct a novel sequential path, which elaborately depicts the post-click behaviors of users.

CLICK-THROUGH RATE PREDICTION

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.

CLICK-THROUGH RATE PREDICTION

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.

CLICK-THROUGH RATE PREDICTION

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.

CLICK-THROUGH RATE PREDICTION