Abstract
Search engine advertising has become the main stream of online advertising system. In current search engine advertising systems, all users will get the same advertisement rank if they use the same query. However, different users may have different degree of interest to each advertisement even though they query the same word. In other words, users prefer to click the interested ad by themselves. For this reason, it is important to be able to accurately estimate the interests of individual users and schedule the advertisements with respect to individual users’ favorites. For users that have rich history queries, their interests can be evaluated using their query logs. For new users, interests are calculated by summarizing the interests of other users who use similar queries. In this paper, we provide a model to automatically learn individual user’s interests based on features of user history queries, user history views of advertisements, user history clicks of advertisements. Then, advertisement schedule is performed according to individual user’s interests in order to raise the clickthrough rate of search engine advertisements in response to each user’s query. We simulate user’s interests of ads and clicks in our experiments. As a result, our personalized ranking scheme of delivering online ads can increase both search engine revenues and users’ satisfactions.
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
Abrams, Z., Mendelevitch, O., Tomlin, J.A.: Optimal Delivery of Sponsored Search Advertisements Subject to Budget Constraints. In: Proceedings 8th ACM Conference on Electronic Commerce (EC 2007), San Diego, California, USA, June 11-15, 2007, pp. 272–278 (2007)
Mahdian, M., Nazerzadeh, H., Saberi, A.: Allocating Online Advertisement Space with Unreliable Estimates. In: Proceedings 8th ACM Conference on Electronic Commerce (EC 2007), San Diego, California, USA, June 11-15, pp. 288–294 (2007)
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, WWW 2007, Banff, Alberta, Canada, May 8-12, pp. 521–530 (2007)
Ghose, A., Yang, S.: An Empirical Analysis of Sponsored Search Performance in Search Engine Advertising. In: Proceedings of the International Conference on Web Search and Web Data Mining, WSDM 2008, Palo Alto, California, USA, February 11-12, pp. 241–250 (2008)
Mehta, A., Saberi, A., Vazirani, U., Vazirani, V.: Adwords and Generalized Online Matching. Journal of ACM 54(5) (October 2007)
Aggarwal, G., Goel, A., Motwani, R.: Truthful Auctions for Pricing Search Keywords. In: Proceedings of the 7th ACM Conference on Electronic Commerce (EC 2006), Ann Arbor, Michigan, USA, June 11-15, pp. 1–7 (2006)
Keerhi, S.S., Tomlin, J.A.: Constructing a maximum utility slate of on-line advertisements. Yahoo! Research, Technical Report, YR-2007-001
Dietrich, B., Forrest, J.J.: A column generation approach for combinatorial auctions. In: Workshop on Mathematics of the Internet: E-Auction and Markets Institute for mathematics and its Applications (2001)
Pass, G., Chowdhury, A., Torgeson, C.: A Picture of Search. In: Proceedings of the first International Conference on Scalable Information Systems, Hong Kong (June 2006)
Qiu, F., Cho, J.: Automatic Identification of User Interest for Personalized Search. In: Proceedings of ACM WWW 2006, Edinburgh, Scotland, MAY 23-26 (2006)
Piwowarski, B., Zaragoza, H.: Predictive User Click Models Based on Click-through History. In: Proceedings of ACM CIKM 2007, Lisboa, Portugal, November 6-8 (2007)
http://adwords.google.com/support/bin/answer.py?answer=10215
Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. In: Proceedings of the Second Workshop on Sponsored Search Auctions, Ann Arbor, MI (June 2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiao, G., Gong, Z. (2009). Personalized Delivery of On–Line Search Advertisement Based on User Interests. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-00672-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00671-5
Online ISBN: 978-3-642-00672-2
eBook Packages: Computer ScienceComputer Science (R0)