Search Results for author:
Found 4 papers, 4 papers with code
MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
In this paper, we propose multi-Hash Codebook NETwork (HCNet) as the memory mechanism for efficiently learning and memorizing representations of cross features in CTR tasks.
Ranked #1 on
Click-Through Rate Prediction
on KDD12
FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
Click-Through Rate (CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed.
Ranked #15 on
Click-Through Rate Prediction
on Criteo
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
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.
