The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Practical Lessons from Predicting Clicks on Ads at Facebook
Abstract
Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising systems. With over 750 million daily active users and over 1 million active advertisers, predicting clicks on Facebook ads is a challenging machine learning task. In this paper we introduce a model which combines decision trees with logistic regression, outperforming either of these methods on its own by over 3%, an improvement with significant impact to the overall system performance. We then explore how a number of fundamental parameters impact the final prediction performance of our system. Not surprisingly, the most important thing is to have the right features: those capturing historical information about the user or ad dominate other types of features. Once we have the right features and the right model (decisions trees plus logistic regression), other factors play small roles (though even small improvements are important at scale). Picking the optimal handling for data freshness, learning rate schema and data sampling improve the model slightly, though much less than adding a high-value feature, or picking the right model to begin with.
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Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
XGBoost: A Scalable Tree Boosting System
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Deep Neural Networks for YouTube Recommendations
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Update or Wait: How to Keep Your Data Fresh
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“Morever, data pipelines are often not static [3]: new data is collected regularly and incrementally used to further refine existing models following the online learning paradigm [4].”
Background
Metadata
- Authors
- He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., Atallah, A., Herbrich, R., Bowers, S., & Candela, J. Q.
- Venue
- Proceedings of the Eighth International Workshop on Data Mining for Online Advertising
- Publisher
- ACM
- Published
- 2014-08-24
- Pages
- 1-9
- OpenAlex
- W2076618162
- Cited by
- 869
- References
- 15
- FWCI
- 30.78
- Type
- conference paper
- Language
- en
- Open access
- closed
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- He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., Atallah, A., Herbrich, R., Bowers, S., & Candela, J. Q. (2014). Practical Lessons from Predicting Clicks on Ads at Facebook. Proceedings of the Eighth International Workshop on Data Mining for Online Advertising. https://doi.org/10.1145/2648584.2648589
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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