Name
Abstract | ||
|---|---|---|
Recommendation systems and computing advertisements have gradually entered the field of academic research from the field of commercial applications. Click-through rate prediction is one of the core research issues because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Feature engineering is very important to improve click-through rate prediction. Traditional feature engineering heavily relies on peopleu0027s experience, and is difficult to construct a feature combination that can describe the complex patterns implied in the data. This paper combines traditional feature combination methods and deep neural networks to automate feature combinations to improve the accuracy of click-through rate prediction. We propose a mechannism named u0027Field-aware Neural Factorization Machineu0027 (FNFM). This model can have strong second order feature interactive learning ability like Field-aware Factorization Machine, on this basis, deep neural network is used for higher-order feature combination learning. Experiments show that the model has stronger expression ability than current deep learning feature combination models like the DeepFM, DCN and NFM. |
Year | Venue | Field |
|---|---|---|
2019 | arXiv: Learning | Click-through rate,Computer science,Theoretical computer science,Factorization,Distributed computing |
DocType | Volume | Citations |
Journal | abs/1902.09096 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Zhang | 1 | 141 | 20.37 |
| Weichen Shen | 2 | 9 | 1.22 |
| Shijian Li | 3 | 1155 | 69.34 |
| Gang Pan | 4 | 1501 | 123.57 |