Mining advertiser-specific user behavior using adfactors

Proceedings of the 19th International Conference on World Wide Web, WWW '10

Volumn , Issue , 2010, Pages 31-40

  Archak, Nikolay ^a   Mirrokni, Vahab S ^b   Muthukrishnan, S ^b  

^a NEW YORK UNIVERSITY   (United States)

^b GOOGLE INC   (United States)

Author keywords

ad auctions; clickthrough rate; conversion rate; online advertising; pagerank; sponsored search; user behavior models

Indexed keywords

BEHAVIORAL PATTERNS; BRAND AWARENESS; BUDGET ALLOCATION; CLICK-THROUGH RATE; CONVERSION RATES; DATA SETS; GRAPH STRUCTURES; LOCAL ALGORITHM; LOCAL CORRELATIONS; LONG-TERM EFFECTS; ONLINE ADVERTISING; PAGERANK; PROGRAMMING MODELS; SCORING RULES; STRUCTURAL CORRELATION; USER BEHAVIORS;

BUDGET CONTROL; COMMERCE; COMPUTATION THEORY; DATA PROCESSING; REGRESSION ANALYSIS; WORLD WIDE WEB;

BEHAVIORAL RESEARCH;

EID: 77954575656     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1772690.1772695     Document Type: Conference Paper

References (25)

1. E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 19-26, 2006.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Databases (VLDB'94), pages 487-499, 1994.
3. H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19:716-723, Dec 1974.
4. R. Andersen, C. Borgs, J. Chayes, J. Hopcroft, V. Mirrokni, and S.-H. Teng. Local computation of pagerank contributions. Internet Mathematics, 5:23-45, Jan. 2009.
5. Atlas. Engagement mapping. Thought Paper, Available at http://www.atlassolutions.com/uploadedFiles/Atlas/Atlas\-Institute/Engagement\ -Mapping/eMapping-TP.pdf, 2008.
6. S. Baluja, R. Seth, D. Sivakumar, Y. Jing, J. Yagnik, S. Kumar, D. Ravichandran, and M. Aly. Video suggestion and discovery for youtube: taking random walks through the view graph. In Proceedings of the 17th International Conference on World Wide Web, WWW, pages 895-904, 2008.
7. H. Cao, D. Jiang, J. Pei, E. Chen, and H. Li. Towards context-aware search by learning a very large variable length hidden markov model from search logs. In WWW '09: Proceedings of the 18th international conference on World wide web, pages 191-200, 2009.
8. comScore. Whither the click? comscore brand metrix norms prove 'view-thru' value of on-line advertising. Available at http://www.comscore.com/ press/release.asp?press=2587, 2008.
9. G. Cormode, F. Korn, S. Muthukrishnan, and Y. Wu. On signatures for communication graphs. In Proceedings of the 24th International Conference on Data Engineering, ICDE, pages 189-198, 2008.
10. E. E. Dar, Y. Mansour, V. Mirrokni, S. Muthukrishnan, and U. Nadav. Budget optimization for broad-match ad auctions. In WWW, World Wide Web Conference, 2009.
11. J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of ACM, 51(1):107-113, 2008.
12. T. Fawcett and F. J. Provost. Combining data mining and machine learning for effective user profiling. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD), pages 8-13, 1996.
13. J. Feldman, S. Muthukrishnan, M. Pal, and C. Stein. Budget optimization in search-based advertising auctions. In EC '07: Proceedings of the 8th ACM conference on Electronic commerce, pages 40-49, 2007.
14. A. Hassan, R. Jones, and K. Klinkner. Beyond DCG: User behavior as a predictor of a successful search. In WSDM '10: Proceedings of the 3rd international conference on web search and data mining, 2010. Forthcoming.
15. T. H. Haveliwala. Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering, 15(4):784-796, 2003.
16. A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In Principles of Data Mining and Knowledge Discovery, pages 13-23, 2000.
17. G. Jeh and J. Widom. Scaling personalized web search. In WWW '03: Proceedings of the 12th international conference on World Wide Web, pages 271-279, 2003.
18. K. Keller. Brand equity and integrated communication. In Integrated Communication: Synergy of Persuasive Voices, 1996.
19. R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pages 1137-1145, 1995.
20. R. Lewis and D. Reiley. Retail advertising works!: Measuring the effects of advertising on sales via a controlled experiment on yahoo! Working paper, Yahoo! Research, 2009.
21. G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. Pregel: a system for large-scale graph processing. In PODC '09: Proceedings of the 28th ACM symposium on Principles of distributed computing, pages 6-6, 2009.
22. H. Mannila, H. Toivonen, and I. Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1:259-289, Sept. 1997. (Pubitemid 127721244)
23. S. Muthukrishnan, M. Pal, and Z. Svitkina. Stochastic models for budget optimization in search-based advertising. In Lecture Notes in Computer Science, Internet and Network Economics, pages 131-142, 2007.
24. L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical Report. Stanford InfoLab., 1999.
25. K. Sugiyama, K. Hatano, and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In WWW '04: Proceedings of the 13th international conference on World Wide Web, pages 675-684, 2004.