2017 GBCENTGradientBoostedCategorica

• (Zhao et al., 2017) ⇒ Qian Zhao, Yue Shi, and Liangjie Hong. (2017). “GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees.” In: Proceedings of the 26th International Conference on World Wide Web. ISBN:978-1-4503-4913-0 doi:10.1145/3038912.3052668

Subject Headings: GB-CENT.

Notes

• Recent presentation: http://hongliangjie.com/talks/GB-CENT_CAS_2017-06-23.pdf

Cited By

• http://scholar.google.com/scholar?q=%222017%22+GB-CENT%3A+Gradient+Boosted+Categorical+Embedding+and+Numerical+Trees
• http://dl.acm.org/citation.cfm?id=3038912.3052668&preflayout=flat#citedby

Quotes

Abstract

Latent factor models and decision tree based models are widely used in tasks of prediction, ranking and recommendation. Latent factor models have the advantage of interpreting categorical features by a low-dimensional representation, while such an interpretation does not naturally fit numerical features. In contrast, decision tree based models enjoy the advantage of capturing the nonlinear interactions of numerical features, while their capability of handling categorical features is limited by the cardinality of those features. Since in real-world applications we usually have both abundant numerical features and categorical features with large cardinality (e.g. geolocations, IDs, tags etc.), we design a new model, called GB-CENT, which leverages latent factor embedding and tree components to achieve the merits of both while avoiding their demerits. With two real-world data sets, we demonstrate that GB-CENT can effectively (i.e. fast and accurately) achieve better accuracy than state-of-the-art matrix factorization, decision tree based models and their ensemble.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 GBCENTGradientBoostedCategorica	Liangjie Hong Yue Shi Qian Zhao			GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees				10.1145/3038912.3052668		2017