To swing or not to swing: learning when (not) to advertise

ABSTRACT

Web textual advertising can be interpreted as a search problem over the corpus of ads available for display in a particular context. In contrast to conventional information retrieval systems, which always return results if the corpus contains any documents lexically related to the query, in Web advertising it is acceptable, and occasionally even desirable, not to show any results. When no ads are relevant to the user's interests, then showing irrelevant ads should be avoided since they annoy the user and produce no economic benefit. In this paper we pose a decision problem "whether to swing", that is, whether or not to show any of the ads for the incoming request. We propose two methods for addressing this problem, a simple thresholding approach and a machine learning approach, which collectively analyzes the set of candidate ads augmented with external knowledge. Our experimental evaluation, based on over 28,000 editorial judgments, shows that we are able to predict, with high accuracy, when to "swing" for both content match and sponsored search advertising.

REFERENCES

• 1: Y. Al-Onaizan, J. Curin, M. Jahr, K. Knight, J. Lafferty, D. Melamed, F.-J. Och, D. Purdy, N. A. Smith, and D. Yarowsky. Statistical machine translation, final report, JHU workshop, 1999.
• 2: Giuseppe Attardi , Andrea Esuli , Maria Simi, Best bets: thousands of queries in search of a client, Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, May 19-21, 2004, New York, NY, USA [doi>10.1145/1013367.1013506]
• 3: Andrei Broder , Marcus Fontoura , Vanja Josifovski , Lance Riedel, A semantic approach to contextual advertising, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277837]
• 4: Peter F. Brown , John Cocke , Stephen A. Della Pietra , Vincent J. Della Pietra , Fredrick Jelinek , John D. Lafferty , Robert L. Mercer , Paul S. Roossin, A statistical approach to machine translation, Computational Linguistics, v.16 n.2, p.79-85, June 1990
• 5: Peter F. Brown , Vincent J. Della Pietra , Stephen A. Della Pietra , Robert L. Mercer, The mathematics of statistical machine translation: parameter estimation, Computational Linguistics, v.19 n.2, June 1993
• 6: Chris Buckley , Ellen M. Voorhees, Retrieval evaluation with incomplete information, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009000]
• 7: J. Carrasco, D. Fain, K. Lang, and L. Zhukov. Clustering of bipartite advertiser-keyword graph. In ICDM Workshop on Clustering Large Datasets. IEEE Comp. Soc. Press, 2003.
• 8: Patrali Chatterjee , Donna L. Hoffman , Thomas P. Novak, Modeling the Clickstream: Implications for Web-Based Advertising Efforts, Marketing Science, v.22 n.4, p.520-541, October 2003 [doi>10.1287/mksc.22.4.520.24906]
• 9: Massimiliano Ciaramita , Vanessa Murdock , Vassilis Plachouras, Online learning from click data for sponsored search, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367529]
• 10: M. Ciaramita, V. Murdock, and V. Plachouras. Semantic associations for contextual advertising. International Journal of Electronic Commerce Research - Special Issue on Online Advertising and Sponsored Search, 2008. To Appear.
• 11: Steve Cronen-Townsend , Yun Zhou , W. Bruce Croft, Predicting query performance, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564429]
• 12: Juan Feng , Hemant K. Bhargava , David M. Pennock, Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms, INFORMS Journal on Computing, v.19 n.1, p.137-148, January 2007 [doi>10.1287/ijoc.1050.0135]
• 13: K. Gallagher, D. Foster, and J. Parsons. The medium is not the message: Advertising effectiveness and content evaluation in print and on the web. Journal Of Advertising Research, 41(4):57--70, 2001.
• 14: B. He and I. Ounis. Inferring query performance using pre-retrieval predictors. In 11th Symposium on String Processing and Information Retrieval, 2004.
• 15: Xin Jin , Ying Li , Teresa Mah , Jie Tong, Sensitive webpage classification for content advertising, Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising, p.28-33, August 12-12, 2007, San Jose, California [doi>10.1145/1348599.1348604]
• 16: Thorsten Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, Kluwer Academic Publishers, Norwell, MA, 2002
• 17: Thorsten Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775067]
• 18: Anísio Lacerda , Marco Cristo , Marcos André Gonçalves , Weiguo Fan , Nivio Ziviani , Berthier Ribeiro-Neto, Learning to advertise, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148265]
• 19: Victor Lavrenko , W. Bruce Croft, Relevance based language models, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.120-127, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383972]
• 20: R. Manmatha , T. Rath , F. Feng, Modeling score distributions for combining the outputs of search engines, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.267-275, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.384005]
• 21: D. Metzler, S. Dumais, and C. Meek. Similarity measures for short segments of text. In ECIR, pages 16--27, 2007.
• 22: Mark Montague , Javed A. Aslam, Relevance score normalization for metasearch, Proceedings of the tenth international conference on Information and knowledge management, October 05-10, 2001, Atlanta, Georgia, USA [doi>10.1145/502585.502657]
• 23: Vanessa Murdock , Massimiliano Ciaramita , Vassilis Plachouras, A noisy-channel approach to contextual advertising, Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising, p.21-27, August 12-12, 2007, San Jose, California [doi>10.1145/1348599.1348603]
• 24: Berthier Ribeiro-Neto , Marco Cristo , Paulo B. Golgher , Edleno Silva de Moura, Impedance coupling in content-targeted advertising, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076119]
• 25: Mehran Sahami , Timothy D. Heilman, A web-based kernel function for measuring the similarity of short text snippets, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135834]
• 26: C. Wang, P. Zhang, R. Choi, and M. D. Eredita. Understanding consumers attitude toward advertising. In Proc. of the 8th Americas Conf. on Information System, pages 1143--1148, 2002.
• 27: Wen-tau Yih , Joshua Goodman , Vitor R. Carvalho, Finding advertising keywords on web pages, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135813]
• 28: Chan Yun Yoo , Patricia A. Stout, Preattentive processing of web advertising, University of Texas at Austin, Austin, TX, 2005
• 29: Yun Zhou , W. Bruce Croft, Query performance prediction in web search environments, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277835]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.m Miscellaneous

General Terms:
Algorithms, Experimentation

Keywords:
ad selection, result quality prediction, web advertising