Discovering Coverage Patterns for Banner Advertisement Placement

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• 作者：P. Gowtham Srinivas (1) gowtham.srinivas@research.iiit.ac.in
  P. Krishna Reddy (1) pkreddy@iiit.ac.in
  S. Bhargav (1) bhargav.spg08@research.iiit.ac.in
  R. Uday Kiran (1) uday_rage@research.iiit.ac.in
  D. Satheesh Kumar (1) satheesh.kumar@research.iiit.ac.in
• 关键词：Click stream mining &#8211 ; online advertising &#8211 ; internet monetization &#8211 ; computational advertising &#8211 ; graphical ads delivery
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7302
• 期：1
• 页码：133-144
• 全文大小：495.3 KB
• 参考文献：1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc. (1994)
  2. Chvatal, V.: A greedy heuristic for the set-covering problem. Mathematics of Operations Research, 233–235 (1979)
  3. Garey, M.R., Johnson, D.S., Stockmeyer, L.: Some simplified np-complete problems. In: Proceedings of the Sixth Annual ACM Symposium on Theory of Computing, STOC 1974, pp. 47–63. ACM (1974)
  4. Venetis, P., Koutrika, G., Garcia-Molina, H.: On the selection of tags for tag clouds. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 835–844. ACM (2011)
  5. Bonchi, F., Castillo, C., Donato, D., Gionis, A.: Topical query decomposition. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 52–60. ACM (2008)
  6. Amiri, A., Menon, S.: Efficient scheduling of internet banner advertisements. ACM Trans. Internet Technol. 3(4), 334–346 (2003)
  7. Ghosh, A., Rubinstein, B.I., Vassilvitskii, S., Zinkevich, M.: Adaptive bidding for display advertising. In: WWW 2009: Proceedings of the 18th International Conference on World Wide Web, pp. 251–260. ACM (2009)
  8. Wu, X., Bolivar, A.: Keyword extraction for contextual advertisement. In: WWW 2008: Proceeding of the 17th International Conference on World Wide Web, pp. 1195–1196. ACM (2008)
  9. Chakrabarti, D., Agarwal, D., Josifovski, V.: Contextual advertising by combining relevance with click feedback. In: WWW 2008: Proceeding of the 17th International Conference on World Wide Web, pp. 417–426. ACM (2008)
  10. Alaei, S., Arcaute, E., Khuller, S., Ma, W., Malekian, A., Tomlin, J.: Online allocation of display advertisements subject to advanced sales contracts. In: ADKDD 2009: Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising, pp. 69–77. ACM (2009)
  11. Sripada, B., Reddy, P.K., Kiran, R.U.: Coverage patterns for efficient banner advertisement placement. In: WWW (Companion Volume), pp. 131–132 (2011)
  12. Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: KDD 1999: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341. ACM (1999)
  13. Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2006)
  14. Fimi: Frequent itemset mining implementations repository (July 2010), http://fimi.cs.helsinki.fi/
  15. Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
  16. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD 1993, pp. 207–216. ACM (1993)
• 作者单位：1. International Institute of Information Technology, Hyderabad, India
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

We propose a model of coverage patterns and a methodology to extract coverage patterns from transactional databases. We have discussed how the coverage patterns are useful by considering the problem of banner advertisements placement in e-commerce web sites. Normally, advertiser expects that the banner advertisement should be displayed to a certain percentage of web site visitors. On the other hand, to generate more revenue for a given web site, the publisher has to meet the coverage demands of several advertisers by providing appropriate sets of web pages. Given web pages of a web site, a coverage pattern is a set of pages visited by a certain percentage of visitors. The coverage patterns discovered from click-stream data could help the publisher in meeting the demands of several advertisers. The efficiency and advantages of the proposed approach is shown by conducting experiments on real world click-stream data sets.