Forecast of Mobile Ad Click through Logistic Regression Algorithm

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2017 by IJPTT Journal
	Volume-7 Issue-5
	Year of Publication : 2017
	Authors : M.Sree Vani

Citation

M.Sree Vani "Forecast of Mobile Ad Click through Logistic Regression Algorithm". International Journal of P2P Network Trends and Technology (IJPTT).V7:30-33 September to October 2017. ISSN:2249-2615. www.ijpttjournal.org. Published by Seventh Sense Research Group.

Abstract

Mobile advertising gives opportunities for advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a result, click prediction systems are central to most mobile advertising systems. With millions of mobile user’s daily activities and active advertisers, predicting clicks on Mobile ads is a challenging machine learning task. This paper presents an experimental study of using different machine learning techniques to predict whether an mobile ad will be clicked or not. Our approach is applied on Avazu mobile Ad click data set. In this paper feature selection is performed to remove features that do not help improve classifier accuracy. Several supervised classification algorithms are applied in experiments and observe that logistic regression with feature selection produces better classification accuracy.

References

[1] Vowpal Wabbit open tool. http://hunch.net/˜vw/. Accessed: 2010-09-30.
[2] John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121–2159, 2011.
[3] Zhipeng Fang, Kun Yue, Jixian Zhang, Dehai Zhang, and Weiyi Liu. Predicting click-through rates of new advertisements based on the bayesian network. Mathematical Problems in Engineering,2014, 2014.
[4] Dejian Lai. Principal component analysis on human development indicators of china. Social
[5] Indicators Research, 61(3):319–330, 2003.
[6] David D Lewis. Naive (bayes) at forty: The independence assumption in information retrieval.In Machine learning: ECML-98, pages 4–15. Springer, 1998.5
[7] H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady,Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et al. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on
[8] Knowledge discovery and data mining, pages 1222–1230. ACM, 2013.
[9] Steffen Rendle. Scaling factorization machines to relational data. In Proceedings of the VLDB Endowment, volume 6, pages 337–348. VLDB Endowment, 2013.6

Keywords
Mobile, Logistic, regression