Study of click through rate prediction in online advertisement

XIAO YAO; BI Jun-fang; HAN YI; DONG Qi-wen

doi:10.3969/j.issn.1000-5641.2017.05.008

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XIAO YAO, BI Jun-fang, HAN YI, DONG Qi-wen. Study of click through rate prediction in online advertisement[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 80-86, 100. doi: 10.3969/j.issn.1000-5641.2017.05.008

Citation:

XIAO YAO, BI Jun-fang, HAN YI, DONG Qi-wen. Study of click through rate prediction in online advertisement[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 80-86, 100. doi: 10.3969/j.issn.1000-5641.2017.05.008

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Study of click through rate prediction in online advertisement

doi: 10.3969/j.issn.1000-5641.2017.05.008

1.
School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
2.
Yangtze River Estuary Survey Bureau of Hydrology and Water Resource, CWRC, Ministry of Water Resources, Shanghai 200136, China

Received Date: 2017-05-01
Publish Date: 2017-09-25

Abstract

Abstract

With the development of the Internet and the growth of users, the advertising industry originated from the traditional offline advertising model, is gradually transforming into online advertising model. At the same time, due to the use of large data analysis technology, online advertising shows great advantages when compared with traditional advertising. The advertisers deliver their advertisements to the platform's specific positions by competition auction of counterparts. Therefore, it is important to predict the click through rate (CTR) of a given advertisement before auction, which is important for advertisers to reduce costs and expand their likely revenue.This paper introduces the commonly used ad click rate prediction model, uses the information from different advertisers, advertisements and media platforms as the features of machine learning, and uses real data sets to illustrate the advantages of various models, and the impact of different features on the ad click rate.
- computational advertising,
- CTR,
- machine learning

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References(20)

References

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