Skip to main content
SpringerLink
Log in

The Dynamically Modified BoW Algorithm Used in Assessing Clicks in Online Ads

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

Included in the following conference series:

Abstract

In this paper we present an algorithm that identifies fraud in online advertising systems such as CPC (Cost Per Click also called PPC Pay-Per-Click). This model used in online advertising is particularly sensitive because it can be exploited by making invalid clicks on an advertisement. This results in additional costs for the advertiser, reduced possibility of reaching the most interested viewers and fraudulent results of an advertising campaign. The dynamically modified BoW (Bag-Of-Words) algorithm presented in the article allows us to identify repetitive clicks made by dishonest publishers or by automatic software, i.e. bots. The algorithm uses data obtained directly on an advertiser’s website. The paper also presents the results of an experimental research confirming effectiveness of the proposed methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gabryel, M.: Data analysis algorithm for click fraud recognition. In: Damaševičius, R., Vasiljevienė, G. (eds.) ICIST 2018. CCIS, vol. 920, pp. 437–446. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99972-2_36

    Chapter  Google Scholar 

  2. https://www.google.com/ads/adtrafficquality/index.html. 23.12.2018

  3. http://blog.pixalate.com/desktop-ad-click-fraud-rising-stats-data-2017. Accessed 23 Dec 2018

  4. https://github.com/Valve/fingerprintjs2. Accessed 23 Dec 2018

  5. https://support.google.com/adwords/answer/42995?hl=en. Accessed 23 Dec 2018

  6. Bilski, J., Kowalczyk, B., Żurada, J.M.: Application of the givens rotations in the neural network learning algorithm. In: Rutkowski, L., et al. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9692, pp. 46–56. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39378-0_5

    Chapter  Google Scholar 

  7. Deepak, D., Simone, A.L.: Effect of strategy adaptation on differential evolution in presence and absence of parameter adaptation: an investigation. J. Artif. Intell. Soft Comput. Res. 8(3), 211–235 (2018). https://doi.org/10.1515/jaiscr-2018-014

    Article  Google Scholar 

  8. Tambouratzis, G.: Using particle swarm optimization to accurately identify syntactic phrases in free text. J. Artif. Intell. Soft Comput. Res. 8(1), 63–67 (2018). https://doi.org/10.1515/jaiscr-2018-0004

    Article  Google Scholar 

  9. Neal, A., Kouwenhoven, S, SA., O.: Quantifying online advertising fraud: Ad-click bots vs humans. Technical. report, Oxford Bio Chronometrics, 2015

    Google Scholar 

  10. Chang, O., Constante, P., Gordon, A., Singana, M.: A novel deep neural network that uses space-time features for tracking and recognizing a moving object. J. Artif. Intell. Soft Comput. Res. 7(2), 125–136 (2017). https://doi.org/10.1515/jaiscr-2017-0009

    Article  Google Scholar 

  11. Riid, A., Preden, J.-S.: Design of fuzzy rule-based classifiers through granulation and consolidation. J. Artif. Intell. Soft Comput. Res. 7(2), 137–147 (2017). https://doi.org/10.1515/jaiscr-2017-0010

    Article  Google Scholar 

  12. Zhu, X., et al.: Fraud Prevention in Online Digital Advertising. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-56793-8

    Book  Google Scholar 

  13. Seyyar, M.B., Çatak, F.Ö., Gül, E.: Detection of attack-targeted scans from the Apache HTTP Server access logs. Appl. Comput. Inf. 14(1), 28–36 (2018)

    Google Scholar 

  14. AsSadhan, B., Moura, J., Lapsley, D., Jones, C., Strayer, W.: Detecting botnets using command and control traffic. In: Eighth IEEE International Symposium on Network Computing and Applications, 2009. NCA, pp. 156–162 (2009)

    Google Scholar 

  15. Korytkowski, M.: Novel visual information indexing in relational databases. Integr. Comput.-Aided Eng. 24(2), 119–128 (2017)

    Article  Google Scholar 

  16. Gabryel, M.: A bag-of-features algorithm for applications using a NoSQL database. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 332–343. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46254-7_26

    Chapter  Google Scholar 

  17. Starczewski, A.: A new validity index for crisp clusters. Pattern Anal. Appl. 20(3), 687–700 (2017)

    Article  MathSciNet  Google Scholar 

  18. Łapa, K., Cpałka, K., Wang, L.: New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8467, pp. 217–232. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07173-2_20

    Chapter  Google Scholar 

  19. Zalasiński, M., Cpałka, K.: Novel algorithm for the on-line signature verification using selected discretization points groups. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7894, pp. 493–502. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38658-9_44

    Chapter  Google Scholar 

  20. Nowicki, R.K., Starczewski, J.T.: A new method for classification of imprecise data using fuzzy rough fuzzification. Inf. Sci. 414, 33–52 (2017)

    Article  Google Scholar 

  21. Starczewski, J.T.: Centroid of triangular and Gaussian type-2 fuzzy sets. Inf. Sci. 280, 289–306 (2014)

    Article  MathSciNet  Google Scholar 

  22. Korytkowski, M., Scherer, R., Staszewski, P., Woldan, P.: Bag-of-features image indexing and classification. In: Microsoft SQL Server Relational Database, pp. 478–482 (2015). https://doi.org/10.1109/cybconf.2015.7175981

  23. Bilski, J., Wilamowski, B.M.: Parallel Levenberg-Marquardt algorithm without error backpropagation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 25–39. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_3

    Chapter  Google Scholar 

  24. Dziwiński, P., Bartczuk, Ł., Przybyszewski, K.: A population based algorithm and fuzzy decision trees for nonlinear modeling. In: Rutkowski, L., et al. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10842, pp. 516–531. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91262-2_46

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Marcin Gabryel

  2. Information Technology Institute, University of Social Sciences, 90-113, Łodz, Poland

    Krzysztof Przybyszewski

  3. Clark University, Worcester, MA, 01610, USA

    Krzysztof Przybyszewski

Authors
  1. Marcin Gabryel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krzysztof Przybyszewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcin Gabryel .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gabryel, M., Przybyszewski, K. (2019). The Dynamically Modified BoW Algorithm Used in Assessing Clicks in Online Ads. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20915-5_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation