Search Results for author:
Found 6 papers, 4 papers with code
Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction
For example, the continuous features are usually transformed to the power forms by adding a new feature to allow it to easily form non-linear functions of the feature.
ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
In this paper, We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions by dynamically refining each feature's embedding according to the input context.
Ranked #14 on
Click-Through Rate Prediction
on Criteo
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper.
Ranked #8 on
Click-Through Rate Prediction
on Criteo
BoostingBERT:Integrating Multi-Class Boosting into BERT for NLP Tasks
Our proposed model uses the pre-trained Transformer as the base classifier to choose harder training sets to fine-tune and gains the benefits of both the pre-training language knowledge and boosting ensemble in NLP tasks.
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
Inspired by these observations, we propose a novel model named GateNet which introduces either the feature embedding gate or the hidden gate to the embedding layer or hidden layers of DNN CTR models, respectively.
Ranked #20 on
Click-Through Rate Prediction
on Criteo
Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction
Normalization has become one of the most fundamental components in many deep neural networks for machine learning tasks while deep neural network has also been widely used in CTR estimation field.
