Andrei Broder
Marcus Fontoura
ABSTRACT
Contextual advertising or Context Match (CM) refers to the placement of commercial textual advertisements within the content of a generic web page, while Sponsored Search (SS) advertising consists in placing ads on result pages from a web search engine, with ads driven by the originating query. In CM there is usually an intermediary commercial ad-network entity in charge of optimizing the ad selection with the twin goal of increasing revenue (shared between the publisher and the ad-network) and improving the user experience. With these goals in mind it is preferable to have ads relevant to the page content, rather than generic ads. The SS market developed quicker than the CM market, and most textual ads are still characterized by "bid phrases" representing those queries where the advertisers would like to have their ad displayed. Hence, the first technologies for CM have relied on previous solutions for SS, by simply extracting one or more phrases from the given page content, and displaying ads corresponding to searches on these phrases, in a purely syntactic approach. However, due to the vagaries of phrase extraction, and the lack of context, this approach leads to many irrelevant ads. To overcome this problem, we propose a system for contextual ad matching based on a combination of semantic and syntactic features.
- R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM, 1999. Google Scholar
Digital Library
- Bernhard E. Boser, Isabelle Guyon, and Vladimir Vapnik. A training algorithm for optimal margin classifiers. In Computational Learning Theory, pages 144--152, 1992. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- D. Fain and J. Pedersen. Sponsored search: A brief history. In In Proc. of the Second Workshop on Sponsored Search Auctions, 2006. Web publication, 2006.Google ScholarCross Ref
- S. C. Gates, W. Teiken, and K.-Shin F. Cheng. Taxonomies by the numbers: building high-performance taxonomies. In CIKM '05: Proc. of the 14th ACM intl. conf. on Information and knowledge management, pages 568--577, New York, NY, 2005. ACM. Google ScholarDigital Library
- A. Lacerda, M. Cristo, M. Andre; G., 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313--323. PrenticeHall, 1971.Google Scholar
- P. Sandeep, D. Agarwal, D. Chakrabarti, and V. Josifovski. Bandits for taxonomies: A model-based approach. In In Proc. of the SIAM intl. conf. on Data Mining, 2007.Google Scholar
- T. Santner and D. Duffy. The Statistical Analysis of Discrete Data. Springer-Verlag, 1989.Google ScholarCross Ref
- R. Stata, K. Bharat, and F. Maghoul. The term vectordatabase: fast access to indexing terms for web pages. Computer Networks, 33(1-6):247--255, 2000. Google ScholarDigital Library
- 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.Google Scholar
- 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. Google ScholarDigital Library
Index Terms
- A semantic approach to contextual advertising
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