Context-Aware Deal Size Prediction

  • Anisio Lacerda
  • Adriano Veloso
  • Rodrygo L. T. Santos
  • Nivio Ziviani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8799)

Abstract

Daily deals sites, such as Groupon and LivingSocial, attract millions of customers in the hunt for products and services at substantially reduced prices (i.e., deals). An important aspect for the profitability of these sites is the correct prediction of how many coupons will be sold for each deal in their catalog—a task commonly referred to as deal size prediction. Existing solutions for the deal size prediction problem focus on one deal at a time, neglecting the existence of similar deals in the catalog. In this paper, we propose to improve deal size prediction by taking into account the context in which a given deal is offered. In particular, we propose a topic modeling approach to identify markets with similar deals and an expectation-maximization approach to model intra-market competition while minimizing the prediction error. A systematic set of experiments shows that our approach offers gains in precision ranging from 8.18% to 17.67% when compared against existing solutions.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agarwal, D., Chen, B.-C.: fLDA: matrix factorization through latent dirichlet allocation. In: ACM WSDM, pp. 91–100 (2010)Google Scholar
  2. 2.
    Arabshahi, A.: Undressing groupon: An analysis of the groupon business model (2011), http://www.ahmadalia.com/blog/2011/01/undressing-groupon.html
  3. 3.
    Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Information Processing-Letters and Reviews 11(10), 203–224 (2007)Google Scholar
  4. 4.
    Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: Massive online analysis. The Journal of Machine Learning Research 11, 1601–1604 (2010)Google Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)MATHGoogle Scholar
  6. 6.
    Boyd-Graber, J.L., Blei, D.M., Zhu, X.: A topic model for word sense disambiguation. In: ACM ACL, pp. 1138–1147 (2010)Google Scholar
  7. 7.
    Byers, J., Mitzenmacher, M., Potamias, M., Zervas, G.: A month in the life of groupon. CoRR, abs/1105.0903 (2011)Google Scholar
  8. 8.
    Byers, J., Mitzenmacher, M., Zervas, G.: Daily deals: Prediction, social diffusion, and reputational ramifications. In: ACM WSDM, pp. 543–552 (2012)Google Scholar
  9. 9.
    Byers, J.W., Mitzenmacher, M., Zervas, G.: The groupon effect on yelp ratings: A root cause analysis. In: ACM EC, pp. 248–265 (2012)Google Scholar
  10. 10.
    Dholakia, U.M.: How effective are groupon promotions for business (2010), http://www.ruf.rice.edu/~dholakia
  11. 11.
    Drucker, H., Burges, C.J., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. In: NIPS, pp. 155–161 (1997)Google Scholar
  12. 12.
    Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: ACM RecSys, pp. 61–68 (2009)Google Scholar
  13. 13.
    Kumar, V., Rajan, B.: Social coupons as a marketing strategy: A multifaceted perspective. Journal of the Academy of Marketing Science 40(1), 120–136 (2012)CrossRefGoogle Scholar
  14. 14.
    Lappas, T., Terzi, E.: Daily-deal selection for revenue maximization. In: ACM CIKM, pp. 565–574 (2012)Google Scholar
  15. 15.
    Mitchell, T.M.: Machine learning, vol. 45. McGraw Hill, Burr Ridge (1997)MATHGoogle Scholar
  16. 16.
    Pinto, D., Benedí, J.-M., Rosso, P.: Clustering narrow-domain short texts by using the kullback-leibler distance. In: Gelbukh, A. (ed.) CICLing 2007. LNCS, vol. 4394, pp. 611–622. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Potamias, M.: The warm-start bias of yelp ratings. CoRR (2012)Google Scholar
  18. 18.
    Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the SMO algorithm for svm regression. IEEE Transactions on Neural Networks 11(5), 1188–1193 (2000)CrossRefGoogle Scholar
  19. 19.
    Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing 14(3), 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Tversky, A., Simonson, I.: Context-dependent preferences. Management Science 39(10), 1179–1189 (1993)CrossRefMATHGoogle Scholar
  21. 21.
    Weng, J., Lim, E.-P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: ACM WSDM, pp. 261–270 (2010)Google Scholar
  22. 22.
    Ye, M., Sandholm, T., Wang, C., Aperjis, C., Huberman, B.A.: Collective attention and the dynamics of group deals. In: WWW, pp. 1205–1212 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anisio Lacerda
    • 1
  • Adriano Veloso
    • 1
  • Rodrygo L. T. Santos
    • 1
  • Nivio Ziviani
    • 1
    • 2
  1. 1.Department of Computer ScienceUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Zunnit TechnologiesBelo HorizonteBrazil

Personalised recommendations