Relational click prediction for sponsored search

WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2012, Pages 493-502

  Xiong, Chenyan ^a,d   Wang, Taifeng ^b   Ding, Wenkui ^c   Shen, Yidong ^d   Liu, Tie Yan ^b  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c TSINGHUA UNIVERSITY   (China)

^d INSTITUTE OF SOFTWARE   (China)

Author keywords

Continuous CRF; Online advertising; Relational click prediction; Sponsored search

Indexed keywords

ACCURATE PREDICTION; CLICK-THROUGH RATE; CLOSED FORM SOLUTIONS; CONDITIONAL RANDOM FIELD; CONTINUOUS CRF; INDEPENDENCE ASSUMPTION; INDEPENDENT MODEL; ONLINE ADVERTISING; SPONSORED SEARCHES;

DATA MINING; INFORMATION RETRIEVAL; MAXIMUM LIKELIHOOD ESTIMATION; SEARCH ENGINES; WEBSITES;

FORECASTING;

EID: 84863287362     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2124295.2124355     Document Type: Conference Paper

References (23)

1. D. Agarwal, B.C. Chen, and P. Elango. Spatio-temporal models for estimating click-through rate. In Proceedings of the 18th international conference on World wide web, pages 21-30. ACM, 2009002E
2. R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining, pages 5-14. ACM, 2009.
3. G. Buscher, S.T. Dumais, and E. Cutrell. The good, the bad, and the random: An eye-tracking study of ad quality in web search. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 42-49. ACM, 2010.
4. H. Cheng and E. Cantú-Paz. Personalized click prediction in sponsored search. In Proceedings of the third ACM international conference on Web search and data mining, pages 351-360. ACM, 2010.
5. M. Ciaramita, V. Murdock, and V. Plachouras. Online learning from click data for sponsored search. In Proceeding of the 17th international conference on World Wide Web, pages 227-236. ACM, 2008.
6. C. Danescu-Niculescu-Mizil, A.Z. Broder, E. Gabrilovich, V. Josifovski, and B. Pang. Competing for users' attention: on the interplay between organic and sponsored search results. In Proceedings of the 19th international conference on World wide web, pages 291-300. ACM, 2010.
7. K. David and M. Mohammad. A cascade model for externalities in sponsored search. In Proceedings of the 4th International Workshop on Internet and Network Economics. ACM, 2008.
8. K. Dembczynski, W. Kotlowski, and D. Weiss. Predicting ads click-through rate with decision rules. In Workshop on Targeting and Ranking in Online Advertising, volume 2008. Citeseer, 2008.
9. Z. Dou, S. Hu, K. Chen, R. Song, and J.R. Wen. Multi-dimensional search result diversification. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 475-484. ACM, 2011.
10. B. Edelman, M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. 2005.
11. A. Ghosh and M. Mahdian. Externalities in online advertising. In Proceeding of the 17th international conference on World Wide Web, pages 161-168. ACM, 2008.
12. A. Ghosh and A. Sayedi. Expressive auctions for externalities in online advertising. In Proceedings of the 19th international conference on World wide web, pages 371-380. ACM, 2010.
13. S. Gollapudi, R. Panigrahy, and M. Goldszmidt. Inferring clickthrough rates on ads from click behavior on search results. pages 1-5, 2011.
14. Google. How are ads ranked. In http://www.google.com/support/grants/bin/ answer.py?hl=en&answer=98917.
15. T. Graepel, J.Q. Candela, T. Borchert, and R. Herbrich. Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft?Ss bing search engine. In Proc. 27th Internat. Conf. on Machine Learning. Morgan Kaufmann, San Francisco, CA. Citeseer, 2010.
16. Dustin Hillard, Eren Manavoglu, Hema Raghavan, Chris Leggetter, Erick Cantíš-Paz, and Rukmini Iyer. The sum of its parts: reducing sparsity in click estimation with query segments. In Information Retrieval Journal. Springer, 2011.
17. T. Qin, T.Y. Liu, X.D. Zhang, D.S. Wang, and H. Li. Global ranking using continuous conditional random fields. In Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), 2008.
18. D. Rafiei, K. Bharat, and A. Shukla. Diversifying web search results. In Proceedings of the 19th international conference on World wide web, pages 781-790. ACM, 2010.
19. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web, pages 521-530. ACM, 2007. (Pubitemid 47582281)
20. H.M Wallach. Conditional random fields: An introduction. In Technical report MS-CIS-04-21, University of Pennsylvania, 2004.
21. W. Xu, E. Manavoglu, and E. Cantu-Paz. Temporal click model for sponsored search. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 106-113. ACM, 2010.
22. C.X. Zhai, W.W. Cohen, and J. Lafferty. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 10-17. ACM, 2003.
23. Z.A. Zhu, W. Chen, T. Minka, C. Zhu, and Z. Chen. A novel click model and its applications to online advertising. In Proceedings of the third ACM international conference on Web search and data mining, pages 321-330. ACM, 2010.