Skip to main content

Advertisement

Springer Nature Link
Log in

Coverage pattern based framework to improve search engine advertising

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

Sponsored search has emerged as one of the most dominant forms for advertising on the Web. In sponsored search, advertisers create ad campaigns and bid on the keywords of potential search queries related to a given product or service. It has been observed that search queries follow a long-tail distribution of a small yet fat head of frequent queries and a long and thin tail of infrequent queries. Normally, the advertisers tend to bid on frequent keywords related to search queries. As a result, the ad space of the tail portion of search queries is harder to exploit. In this paper, we have proposed an improved allocation approach to utilize the ad space of the tail keywords related to search queries based on the knowledge of coverage patterns extracted from the transactions formed from search query logs. The advertisers bid on potential concepts represented by coverage patterns which consist of a combination of head and tail keywords. By facilitating the advertisers to bid on the concepts, the proposed approach improves the ad space utilization of tail queries. Experiments on the real-world dataset of search query logs demonstrate that the proposed approach indeed improves the performance of search engine advertising by improving ad space utilization of tail queries.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Broder, A., Ciccolo, P., Gabrilovich, E., Josifovski, V., Metzler, D., Riedel, L., Yuan, J.: Online expansion of rare queries for sponsored search. In: International Conference on World Wide Web, pp. 511–520. ACM (2009)

  2. Broder, A.Z., Ciccolo, P., Fontoura, M., Gabrilovich, E., Josifovski, V., Riedel, L.: Search advertising using web relevance feedback. In: International Conference on Information and Knowledge Management, pp. 1013–1022. ACM (2008)

  3. Broder, A.Z., Fontoura, M., Gabrilovich, E., Joshi, A., Josifovski, V., Zhang, T.: Robust classification of rare queries using web knowledge. In: International Conference on Research and Development in Information Retrieval, pp. 231–238. ACM (2007)

  4. Budhiraja, A., Reddy, P.K.: An approach to cover more advertisers in adwords. In: International Conference on Data Science and Advanced Analytics (DSAA), 2015, pp. 1–10. IEEE (2015)

  5. Chau, M., Fang, X., Liu Sheng, O.R.: Analysis of the query logs of a web site search engine. J. Am. Soc. Inf. Sci. Technol. 56(13), 1363–1376 (2005)

    Article  Google Scholar 

  6. Chvatal, V.: A greedy heuristic for the set-covering problem. Math. Oper. Res. 4(3), 233–235 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  7. Devanur, N.R., Hayes, T.P.: The adwords problem: online keyword matching with budgeted bidders under random permutations. In: Proceedings 10th ACM Conference on Electronic Commerce, pp. 71–78 (2009)

  8. Fain, D.C., Pedersen, J.O.: Sponsored search: a brief history. Bull. Assoc. Inf. Sci. Technol. 32(2), 12–13 (2006)

    Article  Google Scholar 

  9. Garey, M.R., Johnson, D.S., Stockmeyer, L.: Some simplified NP-complete problems. In: Symposium on Theory of Computing, pp. 47–63. ACM (1974)

  10. Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. Omnipress (2010)

  11. Hillard, D., Schroedl, S., Manavoglu, E., Raghavan, H., Leggetter, C.: Improving ad relevance in sponsored search. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 361–370. ACM (2010)

  12. Horowitz, D., Kamvar, S.D.: The anatomy of a large-scale social search engine. In: Proceedings of the 19th International Conference on World Wide Web, pp. 431–440. ACM (2010)

  13. IAB internet advertising revenue report (2015)

  14. Kavya, V.N.S., Reddy, P.K.: Coverage patterns-based approach to allocate advertisement slots for display advertising. In: International Conference on Web Engineering, pp. 152–169. Springer, Berlin (2016)

  15. Lahaie, S.: An analysis of alternative slot auction designs for sponsored search. In: Proceedings of the 7th ACM Conference on Electronic Commerce, pp. 218–227. ACM (2006)

  16. Loper, E., Bird, S.: Natural language toolkit (2005)

  17. Mehta, A.: Online matching and ad allocation. Theor. Comput. Sci. 8(4), 265–368 (2012)

    MathSciNet  MATH  Google Scholar 

  18. Mehta, A., Saberi, A., Vazirani, U., Vazirani, V.: Adwords and generalized online matching. pp. 22–41. ACM (2007)

  19. Mehta, A., et al.: Online matching and ad allocation. Found. Trends Theor. Comput. Sci. 8(4), 265–368 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  20. Mladenow, A., Novak, N.M., Strauss, C.: Online ad-fraud in search engine advertising campaigns. In: Information and Communication Technology—Third IFIP TC 5/8 International Conference, pp. 109–118. Springer, Berlin (2015)

  21. Noll, M.G., Meinel, C.: The metadata triumvirate: social annotations, anchor texts and search queries. In: Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 640–647. IEEE (2008)

  22. Raghavan, H., Iyer, R.: Evaluating vector-space and probabilistic models for query to ad matching. In: SIGIR Workshop on Information Retrieval in Advertising (IRA) (2008)

  23. Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web, pp. 521–530. ACM (2007)

  24. Rutz, O.J., Trusov, M.: Zooming in on paid search ads: a consumer-level model calibrated on aggregated data. Mark. Sci. 30(5), 789–800 (2011)

    Article  Google Scholar 

  25. Srinivas, P.G., Reddy, P.K., Bhargav, S., Kiran, R.U., Kumar, D.S.: Discovering coverage patterns for banner advertisement placement. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 133–144. Springer, Berlin (2012)

  26. Srinivas, P.G., Reddy, P.K., Trinath, A., Bhargav, S., Kiran, R.U.: Mining coverage patterns from transactional databases. J. Intell. Inf. Syst. pp. 1–17 (2014)

  27. Sripada, B., Polepalli, K.R., Rage, U.K.: Coverage patterns for efficient banner advertisement placement. In: International Conference Companion on World Wide Web, pp. 131–132. ACM (2011)

  28. Tolomei, G., Lalmas, M., Farahat, A., Haines, A.: You must have clicked on this ad by mistake! data-driven identification of accidental clicks on mobile ads with applications to advertiser cost discounting and click-through rate prediction. Int. J. Data Sci. Anal. pp. 1–14 (2018)

  29. Trimponias, G., Papadias, D.: A survey on sponsored search advertising in large commercial search engines (2013)

  30. Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)

  31. Zhang, Y., Zhang, W., Gao, B., Yuan, X., Liu, T.Y.: Bid keyword suggestion in sponsored search based on competitiveness and relevance. Inf. Process. Manag. 50(4), 508–523 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kohli Centre on Intelligent Systems, IIIT, Hyderabad, India

    Amar Budhiraja, Akhil Ralla & P. Krishna Reddy

Authors
  1. Amar Budhiraja
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Akhil Ralla
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. P. Krishna Reddy
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Amar Budhiraja.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Budhiraja, A., Ralla, A. & Reddy, P.K. Coverage pattern based framework to improve search engine advertising. Int J Data Sci Anal 8, 199–211 (2019). https://doi.org/10.1007/s41060-018-0165-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41060-018-0165-3

Keywords