An Efficient Deep Interaction Network for Click-Through Rate Prediction
Abstract
References
Index Terms
- An Efficient Deep Interaction Network for Click-Through Rate Prediction
Recommendations
Deep Field Relation Neural Network for click-through rate prediction
AbstractClick-Through Rate (CTR) prediction is crucial in calculating advertisements and recommendation systems. To effectively predict CTR, it is important to properly model the interaction among features of data. This work tends to fully utilise the ...
Attention-Based Feature Interaction Deep Factorization Machine for CTR Prediction
Artificial Neural Networks and Machine Learning – ICANN 2023AbstractClick-Through Rate (CTR) prediction is widely used in many fields, such as web search, recommender systems, etc. Recently, the CTR prediction model using deep learning and attention mechanism technology has achieved remarkable success. However, ...
Deep context interaction network based on attention mechanism for click-through rate prediction
Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad, is a critical task in online advertising systems. The problem is very challenging since(1) an effective prediction relies on high-order combinatorial ...
Comments
Information & Contributors
Information
Published In

In-Cooperation
- Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 86Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in