The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Deep Learning over Multi-field Categorical Data
Abstract
Predicting user responses, such as click-through rate and conversion rate, are critical in many web applications including web search, personalised recommendation, and online advertising. Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known. Major user response prediction models have to either limit themselves to linear models or require manually building up high-order combination features. The former loses the ability of exploring feature interactions, while the latter results in a heavy computation in the large feature space. To tackle the issue, we propose two novel models using deep neural networks (DNNs) to automatically learn effective patterns from categorical feature interactions and make predictions of users' ad clicks. To get our DNNs efficiently work, we propose to leverage three feature transformation methods, i.e., factorisation machines (FMs), restricted Boltzmann machines (RBMs) and denoising auto-encoders (DAEs). This paper presents the structure of our models and their efficient training algorithms. The large-scale experiments with real-world data demonstrate that our methods work better than major state-of-the-art models.
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Cites
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Cited by
Learning From Noisy Labels With Deep Neural Networks: A Survey
Song, H., Kim, M., Park, D., Shin, Y., & Lee, J.-G. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3152527 · Cited by 1,308
Kraus, M., Feuerriegel, S., & Oztekin, A. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.09.018 · Cited by 392
Hierarchical Reinforcement Learning for Course Recommendation in MOOCs
Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., & Sun, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301435 · Cited by 196
Online Display Advertising Markets: A Literature Review and Future Directions
Choi, H., Mela, C. F., Balseiro, S. R., & Leary, A. (2020) · Information Systems Research · doi:10.1287/isre.2019.0902 · Cited by 181
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
Self-citation
Metadata
- Authors
- Zhang, W., Du, T., & Wang, J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 45-57
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2443960221
- Cited by
- 445
- References
- 35
- FWCI
- 27.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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