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Applying Matrix Factorization for Predicting Click Through Rate on Advertizing in Apps on Mobile Devices

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 104)

Abstract

The number of smart phone and smartphone users has increased rapidly, boosting the growth of mobile advertising. Predicting the percentage of users who click on an advertisement when displayed on an application (CTR: Click Through Rate) plays an important role in choosing an advertising push strategy for each application. However, because the field of mobile advertising is still new, the study of solutions to these problems is only for Web advertising. This paper presents a new predictive method based on Matrix Factorization (MF) algorithm. The paper also proposes a method of integrating the reliability of data into MF algorithms. The method was tested on actual data provided by Amobi advertising company with 1.7 million records including 300 advertisements and 400 mobile device applications. The AUC (Area Under The Curve) value obtained on the test set is 0.9037 with MF and 0.9411 when integrating reliability into the MF algorithm. Thus, the results show that the predictive quality is significantly improved when integrating data reliability and evaluation into MF algorithms.

Keywords

  • Click-Through Rate
  • Smartphone application
  • Matrix Factorization
  • In-app mobile advertising

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Correspondence to Tuan-Dung Cao .

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Nguyen, Q.H., Cao, TD. (2020). Applying Matrix Factorization for Predicting Click Through Rate on Advertizing in Apps on Mobile Devices. In: Sattler, KU., Nguyen, D., Vu, N., Tien Long, B., Puta, H. (eds) Advances in Engineering Research and Application. ICERA 2019. Lecture Notes in Networks and Systems, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-030-37497-6_15

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