The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Deep & Cross Network for Ad Click Predictions
Open accessAbstract
Feature engineering has been the key to the success of many prediction models. However, the process is nontrivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessarily efficient in learning all types of cross features. In this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits of a DNN model, and beyond that, it introduces a novel cross network that is more efficient in learning certain bounded-degree feature interactions. In particular, DCN explicitly applies feature crossing at each layer, requires no manual feature engineering, and adds negligible extra complexity to the DNN model. Our experimental results have demonstrated its superiority over the state-of-art algorithms on the CTR prediction dataset and dense classification dataset, in terms of both model accuracy and memory usage.
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Cites
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Cited by
Federated learning for predicting clinical outcomes in patients with COVID-19
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842
“9a, the model fuses information from both EMR and CXR features (based on a modified ResNet34 with spatial attention 66 pretrained on the CheXpert dataset) 67 and the Deep & Cross network 68 .”
MethodRecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Zhao, W. X., Mu, S., Hou, Y., Lin, Z., Chen, Y., Pan, X., Li, K., Lu, Y., Wang, H., Tian, C., et al. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3482016 · Cited by 455
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Naumov, M., Mudigere, D., Shi, H.-J. M., Huang, J., Sundaraman, N., Park, J., Wang, X., Gupta, U., Wu, C.-J., Azzolini, A. G., et al. (2019) · arXiv · doi:10.48550/arxiv.1906.00091 · Cited by 411
Liu, B., Zhu, C., Li, G., Zhang, W., Lai, J., Tang, R., He, X., Li, Z., & Yu, Y. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403314 · Cited by 171
A Study of BFLOAT16 for Deep Learning Training
Kalamkar, D., Mudigere, D., Mellempudi, N., Das, D., Banerjee, K., Avancha, S., Vooturi, D. T., Jammalamadaka, N., Huang, J., Yuen, H., et al. (2019) · arXiv · doi:10.48550/arxiv.1905.12322 · Cited by 169
“The accuracy of the recommendation system models is measured by the log loss [38, 43, 29], which predicts when the users will click on ads.”
Method
Metadata
- Authors
- Wang, R., Fu, B., Fu, G., & Wang, M.
- Venue
- Proceedings of the ADKDD'17
- Publisher
- ACM
- Published
- 2017-08-14
- Pages
- 1-7
- OpenAlex
- W2964182926
- Cited by
- 1,161
- References
- 16
- FWCI
- 43.20
- Type
- conference paper
- Language
- en
- Open access
- gold
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