Predicting response in mobile advertising with hierarchical importance-aware factorization machine

Authors:
Richard J. Oentaryo;Ee-Peng Lim;Jia-Wei Low;David Lo;Michael Finegold
Affiliations:
Singapore Management University, Singapore, Singapore;Singapore Management University, Singapore, Singapore;Singapore Management University, Singapore, Singapore;Singapore Management University, Singapore, Singapore;Carnegie Mellon University, Pittsburgh, PA, USA
Venue:
Proceedings of the 7th ACM international conference on Web search and data mining
Year:
2014

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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.