CADCN: A click-through rate prediction model based on feature importance
Abstract
References
Index Terms
- CADCN: A click-through rate prediction model based on feature importance
Recommendations
Enhanced Feature Importance Learning for the Click-Through Rate Prediction
ICMLC '23: Proceedings of the 2023 15th International Conference on Machine Learning and ComputingThe prediction of click-through rate (CTR) is critical in the recommender system, which aims to predict the probability of the user clicking on the recommended item. Considering the increasing number of features used by modern recommender systems, it is ...
AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge ManagementClick-through rate prediction is an important task in commercial recommender systems and it aims to predict the probability of a user clicking on an item. The event of a user clicking on an item is accompanied by several user and item features. As ...
A novel graph-based feature interaction model for click-through rate prediction
AbstractClick-through rate (CTR) prediction is a crucial issue in recommender systems. In addition, data sparsity is a notable challenge for recommender systems compared to other applications. To overcome it, many learning-based models are studied to ...
Highlights- A novel feature interaction model based on graph and FM is studied.
- Graph and FM make feature interactions flexible and learnable.
- Three existing CTR prediction methods are improved by our feature interaction model.
- Experiments ...
Comments
Information & Contributors
Information
Published In
![cover image ACM Other conferences](/cms/asset/24edee37-ce00-4a88-bfa6-5af82fe221b6/3603781.cover.jpg)
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- National Natural Science Foundation of China
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 26Total Downloads
- Downloads (Last 12 months)13
- Downloads (Last 6 weeks)1
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 inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format