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LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign

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Abstract

A lot of online marketing campaigns aim to promote user interaction. The average treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign. A/B testing is usually conducted for such needs, whereas the existence of user interaction can introduce interference to normal A/B testing. With the help of link prediction, we design a network A/B testing method LinkLouvain to minimize graph interference and it gives an accurate and sound estimate of the campaign’s ATE. In this paper, we analyze the network A/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data. Our method achieves significant performance compared with others and is deployed in the online marketing campaign.

T. Cai, D. Cheng and C. Liang—These authors contributed equally.

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References

  1. Cox, D.R., Cox, D.R.: Planning of Experiments, vol. 20. Wiley, New York (1958)

    MATH  Google Scholar 

  2. Deng, A., Knoblich, U., Lu, J.: Applying the delta method in metric analytics: a practical guide with novel ideas. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 233–242 (2018)

    Google Scholar 

  3. Fan, W., et al.: Parallelizing sequential graph computations. ACM Trans. Database Syst. (TODS)43(4), 1–39 (2018)

    Article MathSciNet  Google Scholar 

  4. Gui, H., Xu, Y., Bhasin, A., Han, J.: Network A/B testing: from sampling to estimation. In: Proceedings of the 24th International Conference on World Wide Web, pp. 399–409 (2015)

    Google Scholar 

  5. Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput.20(1), 359–392 (1998)

    Article MathSciNet  Google Scholar 

  6. Kohavi, R., Longbotham, R., Sommerfield, D., Henne, R.M.: Controlled experiments on the web: survey and practical guide. Data Min. Knowl. Disc.18(1), 140–181 (2009)

    Article MathSciNet  Google Scholar 

  7. Li, H., Yuan, H., Huang, J., Cui, J., Yoo, J.: Dynamic graph repartitioning: from single vertex to vertex group. In: Nah, Y., Cui, B., Lee, S.-W., Yu, J.X., Moon, Y.-S., Whang, S.E. (eds.) DASFAA 2020. LNCS, vol. 12113, pp. 482–497. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-59416-9_29

    Chapter  Google Scholar 

  8. Nicoara, D., Kamali, S., Daudjee, K., Chen, L.: Hermes: dynamic partitioning for distributed social network graph databases. In: EDBT, pp. 25–36 (2015)

    Google Scholar 

  9. Stanton, I., Kliot, G.: Streaming graph partitioning for large distributed graphs. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1222–1230 (2012)

    Google Scholar 

  10. Tsourakakis, C., Gkantsidis, C., Radunovic, B., Vojnovic, M.: FENNEL: streaming graph partitioning for massive scale graphs. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 333–342 (2014)

    Google Scholar 

  11. Ugander, J., Karrer, B., Backstrom, L., Kleinberg, J.: Graph cluster randomization: network exposure to multiple universes. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 329–337 (2013)

    Google Scholar 

  12. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, pp. 5165–5175 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Ant Financial Services Group, Hangzhou, China

    Tianchi Cai, Daxi Cheng, Chen Liang, Ziqi Liu, Lihong Gu, Huizhi Xie, Zhiqiang Zhang, Xiaodong Zeng & Jinjie Gu

Authors
  1. Tianchi Cai

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  2. Daxi Cheng

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  3. Chen Liang

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  4. Ziqi Liu

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  5. Lihong Gu

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  6. Huizhi Xie

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  7. Zhiqiang Zhang

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  8. Xiaodong Zeng

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  9. Jinjie Gu

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Corresponding authors

Correspondence toTianchi Cai,Daxi Cheng orChen Liang.

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

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Cite this paper

Cai, T.et al. (2021). LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign. In: Jensen, C.S.,et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_34

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Chapter
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  • Available as PDF
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eBook
JPY 14871
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 18589
Price includes VAT (Japan)
  • Compact, lightweight edition
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Purchases are for personal use only

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