Abstract
How could one estimate the total number of clicks a new advertisement could potentially receive in the current market? This question, called the click volume estimation problem is investigated in this paper. This constitutes a new research direction for advertising engines. We propose a model of computing an estimation of the click volume. A key component of our solution is the application of linear regression to a large (but sparse) data set. We propose an iterative method in order to achieve a fast approximation of the solution. We prove that our algorithm always converges to optimal parameters of linear regression. To the best of our knowledge, it is the first time when linear regression is considered in such a large scale context.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Models for sponsored search: What are the right questions? Panel Discussion at SSA 2006 (2006), http://research.microsoft.com/~hartline/papers/panel-SSA-06.pdf
Bartz, K., Murthi, V., Sebastian, S.: Logistic regression and collaborative filtering for sponsored search term recommendation. In: SSA 2006 (2006)
Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. In: SSA 2006 (2006)
Fain, D., Pedersen, J.: Sponsored search: a brief history. In: SSA 2006 (2006)
Feng, J., Bhargava, H., Pennock, D.: Implementing sponsored search in web search engines: Computational evaluation of alternative mechanisms. Informs Journal on Computing (2006)
Kleinberg, J.M.: Two algorithms for nearest-neighbor search in high dimensions. In: STOC 1997, pp. 599–608 (1997)
Lifshits, Y.: A Guide to Web Research. Materials of mini-course at Stuttgart University (2007), Available at http://logic.pdmi.ras.ru/~yura/webguide.html
Linden, G., Smith, B., York, J.: Amazon.com recommendations item-to-item collaborative filtering. Internet Computing (2003)
Mehta, A., Saberi, A., Vazirani, U., Vazirani, V.: AdWords and generalized on-line matching. In: FOCS 2005, IEEE, New York (2005)
O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: SIGIR 2001, Workshop on Recommender Systems (2001)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods: support vector learning, pp. 185–208. MIT Press, Cambridge, MA, USA (1999)
Regelson, M., Fain, D.: Predicting clickthrough rate using keyword clusters. In: SSA 2006 (2006)
Saad, Y.: Iterative methods for sparse linear systems, 2nd edn. SIAM (2003)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Seber, G.A.F., Lee, A.J.: Linear Regression Analysis. Wiley, Chichester (2003)
Tuzhilin, A.: The Lane’s Gifts v. Google report (2006)
Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: SODA 1993, pp. 311–321 (1993)
Zhdanov, V.: The componentwise descent method. Mathematical Notes 22(1), 566–569 (1977)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lifshits, Y., Nowotka, D. (2007). Estimation of the Click Volume by Large Scale Regression Analysis. In: Diekert, V., Volkov, M.V., Voronkov, A. (eds) Computer Science – Theory and Applications. CSR 2007. Lecture Notes in Computer Science, vol 4649. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74510-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-74510-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74509-9
Online ISBN: 978-3-540-74510-5
eBook Packages: Computer ScienceComputer Science (R0)