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Found 4 papers, 4 papers with code
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
It is crucial to effectively model feature interactions to improve the prediction performance of CTR models.
Ranked #1 on
Click-Through Rate Prediction
on Criteo
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
In addition, we present a new architecture of assigning independent FR modules to separate sub-networks for parallel CTR models, as opposed to the conventional method of inserting a shared FR module on top of the embedding layer.
CL4CTR: A Contrastive Learning Framework for CTR Prediction
Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e. g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance.
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance.
