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Click-Through Rate Prediction

29 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.

( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )

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Latest papers without code

Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution

8 Jan 2020

Click-through rate (CTR) prediction is an essential task in industrial applications such as video recommendation.

CLICK-THROUGH RATE PREDICTION

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

Learning Feature Interactions with Lorentzian Factorization Machine

22 Nov 2019

Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions.

CLICK-THROUGH RATE PREDICTION

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

Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

26 May 2019

It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration.

CALIBRATION 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