Publication Type

Conference Proceeding Article

Publication Date

2-2014

Abstract

Mobile advertising has recently seen dramatic growth, fueled by the global proliferation of mobile phones and devices. The task of predicting ad response is thus crucial for maximizing business revenue. However, ad response data change dynamically over time, and are subject to cold-start situations in which limited history hinders reliable prediction. There is also a need for a robust regression estimation for high prediction accuracy, and good ranking to distinguish the impacts of different ads. To this end, we develop a Hierarchical Importance-aware Factorization Machine (HIFM), which provides an effective generic latent factor framework that incorporates importance weights and hierarchical learning. Comprehensive empirical studies on a real-world mobile advertising dataset show that HIFM outperforms the contemporary temporal latent factor models. The results also demonstrate the efficacy of the HIFM’s importance-aware and hierarchical learning in improving the overall prediction and prediction in cold-start scenarios, respectively.

Keywords

Factorization machine, hierarchy, importance weight, mobile advertising, response prediction

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

WSDM'14: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, February 24-28, 2014, New York

First Page

123

Last Page

132

ISBN

9781450323512

Identifier

10.1145/2556195.2556240

Publisher

ACM

City or Country

New York

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1145/2556195.2556240

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