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Conversion Rate Estimation in Online Advertising via Exploring Potential Impact of Creative

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Database and Expert Systems Applications (DEXA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10439))

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Abstract

As a key criterion to measure ads performance, CVR quantitatively describes the proportion of users who take a desirable action (such as purchasing an item, adding to a cart, adding favorite items, etc.) on given ads in the ads ecosystem. Therefore, it is a critical issue to allocate ads-budget and increase advertisers profits. Focusing on improving the accuracy of CVR prediction in online advertising, this paper firstly analyzes and reveals the correlation underlying creatives associated with ads and CVR, which is excluded by most state-of-the-arts in this literature. Furthermore, we propose a novel LR+ model to utilize the potential impacts of creatives on predicting CVR. Experimental results and analysis on two public real-world datasets (REC-TMALL dataset and Taobao Clothes Matching dataset) validate the effectiveness of the proposed LR+ and demonstrate that the proposed LR+ outperforms typical models (e.g., LR, GBDT and linear SVR) in term of root mean square of error (RMSE).

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Notes

  1. 1.

    https://www.taobao.com.

  2. 2.

    www.flickr.com.

  3. 3.

    Source Code: http://www.cs.unc.edu/~vicente/code.html.

  4. 4.

    https://tianchi.shuju.aliyun.com/datalab/dataSet.htm?spm=5176.100073.888.15. VlfDKh&id=2.

  5. 5.

    https://tianchi.shuju.aliyun.com/datalab/dataSet.htm?spm=5176.100073.888.29. U0CN9W&id=13.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (7167010139).

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Authors and Affiliations

  1. Software School, South China University of Technology, Guangzhou, China

    Junxiang Jiang & Huiqiang Jiang

Authors
  1. Junxiang Jiang
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  2. Huiqiang Jiang
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Corresponding author

Correspondence to Junxiang Jiang .

Editor information

Editors and Affiliations

  1. University of Lyon, Villeurbanne, France

    Djamal Benslimane

  2. University of Milan, Milan, Italy

    Ernesto Damiani

  3. University of Michigan, Dearborn, Michigan, USA

    William I. Grosky

  4. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  5. Wright State University, Dayton, Ohio, USA

    Amit Sheth

  6. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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Jiang, J., Jiang, H. (2017). Conversion Rate Estimation in Online Advertising via Exploring Potential Impact of Creative. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-64471-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64470-7

  • Online ISBN: 978-3-319-64471-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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