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Found 11 papers, 6 papers with code
Ensemble Learning via Knowledge Transfer for CTR Prediction
While many existing methods utilize ensemble learning to improve model performance, they typically limit the ensemble to two or three sub-networks, with little exploration of larger ensembles.
Feature Interaction Fusion Self-Distillation Network For CTR Prediction
Most of the current approaches model feature interactions through stacked or parallel structures, with some employing knowledge distillation for model compression.
Large Language Model Aided QoS Prediction for Service Recommendation
Such capability is potentially useful for the web service recommendation task, where the web users and services have intrinsic attributes that can be described using natural language sentences and are useful for recommendation.
DCNv3: Towards Next Generation Deep Cross Network for CTR Prediction
Deep & Cross Network and its derivative models have become an important paradigm for click-through rate (CTR) prediction due to their effective balance between computational cost and performance.
Ranked #1 on
Click-Through Rate Prediction
on KKBox
Dual-domain Collaborative Denoising for Social Recommendation
The embedding-space collaborative denoising module devotes to resisting the noise cross-domain diffusion problem through contrastive learning with dual-domain embedding collaborative perturbation.
TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation
Specifically, the framework employs the SSEM at the bottom of the model to differentiate between samples, thereby assigning a more suitable encoder for each sample.
Ranked #1 on
Click-Through Rate Prediction
on Frappe
CETN: Contrast-enhanced Through Network for CTR Prediction
Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search.
Ranked #5 on
Click-Through Rate Prediction
on Avazu
CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence
Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way.
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings
External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval.
Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients
Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data is available.
Constraint-based Causal Structure Learning with Consistent Separating Sets
It is achieved by repeating the constraint-based causal structure learning scheme, iteratively, while searching for separating sets that are consistent with the graph obtained at the previous iteration.
