Contextual advertising by combining relevance with click feedback

Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08

Volumn , Issue , 2008, Pages 417-426

  Chakrabarti, Deepayan ^a   Agarwal, Deepak ^a   Josifovski, Vanja ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

Clickthrough rate; Interaction effects; Modeling

Indexed keywords

INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; INFORMATION SERVICES; INTERNET; NATURAL LANGUAGE PROCESSING SYSTEMS; PARALLEL ALGORITHMS; REGRESSION ANALYSIS;

CLICKTHROUGH RATE; COSINE SIMILARITIES; DOCUMENT RETRIEVALS; EXPERIMENTAL EVALUATIONS; FITTING ALGORITHMS; INFORMATION RETRIEVAL MODELS; INTERACTION EFFECTS; LOGISTIC REGRESSION MODELS; MODEL PARAMETERS; MODELING; SCALABLE IMPLEMENTATIONS; SCORING FUNCTIONS; SHRINKAGE ESTIMATORS; TEST DATUMS; TRAINING CORPORA; USER EXPERIENCES;

WORLD WIDE WEB;

EID: 57349199269     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1367497.1367554     Document Type: Conference Paper

References (22)

1. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM, 1999.
2. A. Z. Broder, D. Carmel, M. Herscovici, A. Soffer, and J. Zien. Efficient query evaluation using a two-level retrieval process. In CIKM '03: Proc. of the twelfth intl. conf. on Information and knowledge management, pages 426-434, New York, NY, 2003. ACM.
3. A. Z. Broder, M. Fontoura, V. Josifovski, and L. Riedel. A semantic approach to contextual advertising. In SIGIR, pages 559-566, 2007.
4. P. Chatterjee, D. L. Hoffman, and T. P. Novak. Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science, 22(4):520-541, 2003.
5. C.Lin, R.C.Weng, and S.S.Keerthi. Trust region newton methods for large-scale logistic regression. In International Conference on machine learning, 2007.
6. C.R.Rao. Linear Statistical Inference and its Applications. Wiley-Interscience, 2002.
7. D.C.Liu and J.Nocedal. On the limited memory bfgs method for large scale optimization. Mathmematical Programming, 45:503-528, 1989.
8. S. Derksen and H. J. Keselman. Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. British Journal of Mathematical and Statistical Psychology, 45:265-282, 1992.
9. Online ad spending to total $19.5 billion in 2007. eMarketer, February 2007. Available from http: //www.emarketer.com/Article.aspx?id=1004635.
10. A. Foundation. Apache hadoop project. In lucene. apache, org/hadoop.
11. G.King and L.Zeng. Logistic regression in rare events data. Political Analysis, 9:137-162, 2001.
12. J.Dean and S.Ghemawat. Mapreduce:simplified data processing on large clusters. In Sixth Symposium on Operating System, Design and Implementation, pages 137-150, 2004.
13. A. Lacerda, M. Cristo, M. A. C, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to advertise. In SIGIR '06: Proc. of the 29th annual intl. ACM SIGIR conf, pages 549-556, New York, NY, 2006. ACM.
14. P. McCullagh and J.A.Nelder. Generalized Linear Models. Chapman and Hall, 1989.
15. M.J.Silvapulle. On the existence of maximum likelihood estimates for the binomial response models. Journal of the Royal Statistical Society, Series B, 43:310-313, 1981.
16. P.Komarek and A.W.Moore. Making logistic regression a core data mining tool with tr-irls. In International Conference on Data Mining, pages 685-688, 2005.
17. M. Regelson and D. Fain. Predicting click-through rate using keyword clusters. In In Proc. of the Second Workshop on Sponsored Search Auctions, 2006.
18. B. Ribeiro-Neto, M. Cristo, P. B. Golgher, and E. S. de Moura. Impedance coupling in content-targeted advertising. In SIGIR '05: Proc. of the 28th annual intl. ACM SIGIR conf., pages 496-503. New York, NY, 2005. ACM.
19. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In WWW, pages 521-530, 2007.
20. S.D.Pietra, V.D.Pietra, and J.Lafferty. Inducing features of random fields. IEEE PAMI, 19:380-393, 1997.
21. C. Wang, P. Zhang, R. Choi, and M. D. Eredita. Understanding consumers attitude toward advertising. In Eighth Americas conf. on Information System, pages 1143-1148, 2002.
22. W. Yih, J. Goodman, and V. R. Carvalho. Finding advertising keywords on web pages. In WWW '06: Proc. of the 15th intl. conf. on World Wide Web, pages 213-222, New York, NY, 2006. ACM.