Map: A model-agnostic pretraining framework for click-through rate prediction
With the widespread application of online advertising systems, click-through rate (CTR)
prediction has received more and more attention and research. The most prominent features …
prediction has received more and more attention and research. The most prominent features …
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 …
architectures to model complex feature interactions but neglected the importance of feature …
Towards deeper, lighter and interpretable cross network for ctr prediction
Click Through Rate (CTR) prediction plays an essential role in recommender systems and
online advertising. It is crucial to effectively model feature interactions to improve the …
online advertising. It is crucial to effectively model feature interactions to improve the …
News recommendation based on user topic and entity preferences in historical behavior
H Zhang, Z Shen - Information, 2023 - mdpi.com
A news-recommendation system is designed to deal with massive amounts of news and
provide personalized recommendations for users. Accurately modeling of news and users is …
provide personalized recommendations for users. Accurately modeling of news and users is …
Enhancing cross-domain click-through rate prediction via explicit feature augmentation
Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to
leverage meaningful data from a related domain to help CTR prediction in target domain …
leverage meaningful data from a related domain to help CTR prediction in target domain …
MSLR: A Self-supervised Representation Learning Method for Tabular Data Based on Multi-scale Ladder Reconstruction
Tabular data are widely used for prediction tasks, but they often suffer from the curse of
dimensionality and noise, leading to degradation in the performance and robustness of …
dimensionality and noise, leading to degradation in the performance and robustness of …
Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning
In online recommendation, financial service, etc., the most common application of multi-task
learning (MTL) is the multi-step conversion estimations. A core property of the multi-step …
learning (MTL) is the multi-step conversion estimations. A core property of the multi-step …
Learning multi-granularity semantic interactive representation for joint low-light image enhancement and super-resolution
J Ye, S Liu, C Qiu, Z Zhang - Information Fusion, 2024 - Elsevier
Images captured in challenging conditions often suffer from the co-existence of low contrast
and low resolution. However, most joint enhancement methods focus on fitting a direct …
and low resolution. However, most joint enhancement methods focus on fitting a direct …
AutoEIS: Automatic feature embedding, interaction and selection on default prediction
Deep models have shown the effectiveness in various areas, eg, finance, healthcare and
recommendation system. Among them, default prediction is a major application in the …
recommendation system. Among them, default prediction is a major application in the …
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
Click-through rate (CTR) prediction has become increasingly indispensable for various
Internet applications. Traditional CTR models convert the multi-field categorical data into ID …
Internet applications. Traditional CTR models convert the multi-field categorical data into ID …