research
∙
06/28/2023
Confidence Ranking for CTR Prediction
Model evolution and constant availability of data are two common phenome...
share
research
∙
04/17/2023
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Click-through rate (CTR) prediction is of great importance in recommenda...
research
∙
06/26/2022
A Pre-Computing Solution for Online Advertising Serving
Click-Through Rate (CTR) prediction plays a key role in online advertisi...
research
∙
05/11/2022
NDGGNET-A Node Independent Gate based Graph Neural Networks
Graph Neural Networks (GNNs) is an architecture for structural data, and...
research
∙
04/08/2022
IA-GCN: Interactive Graph Convolutional Network for Recommendation
Recently, Graph Convolutional Network (GCN) has become a novel state-of-...
research
∙
04/01/2022
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction
Click-through rate (CTR) Prediction is of great importance in real-world...
research
∙
04/01/2022
Concept Drift Adaptation for CTR Prediction in Online Advertising Systems
Click-through rate (CTR) prediction is a crucial task in web search, rec...
research
∙
11/09/2021
Dynamic Parameterized Network for CTR Prediction
Learning to capture feature relations effectively and efficiently is ess...
research
∙
10/02/2020
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Click-through rate (CTR) prediction is one of the fundamental tasks for ...
research
∙
06/18/2020