Abstract
Contextual advertising seeks to place relevant textual ads within the content of generic webpages. In this article, we explore a novel semantic approach to contextual advertising. This consists of three tasks: (1) building a well-organized hierarchical taxonomy of topics, (2) developing a robust classifier for effectively finding the topics of pages and ads, and (3) ranking ads based on the topical relevance to pages. First, we heuristically build our own taxonomy of topics from the Open Directory Project (ODP). Second, we investigate how to increase classification accuracy by taking the unique characteristics of the ODP into account. Last, we measure the topical relevance of ads by applying a link analysis technique to the similarity graph carefully derived from our taxonomy. Experiments show that our classification method improves the performance of Ma-F1 by as much as 25.7% over the baseline classifier. In addition, our ranking method enhances the relevance of ads substantially, up to 10% in terms of precision at k, compared to a representative strategy.
- Anagnostopoulos, A., Broder, A., Gabrilovich, E., Josifovski, V., and Riedel, L. 2007. Just-in-time contextual advertising. In Proceedings of the 16th ACM Conference on Information and Knowledge Management (CIKM'07). ACM, New York, NY, 331--340. http://dx.doi.org/10.1145/1321440.1321488. Google Scholar
Digital Library
- Armano, G., Giuliani, A., and Vargiu, E. 2011a. Semantic enrichment of contextual advertising by using concepts. In Proceedings of the 3rd International Conference on Knowledge Discovery and Information Retrieval (KDIR'11). 232--237.Google Scholar
- Armano, G., Giuliani, A., and Vargiu, E. 2011b. Studying the impact of text summarization on contextual advertising. In Proceedings of the 22nd International Workshop on Database and Expert Systems Applications (DEXA'11). 172--176. http://dx.doi.org/10.1109/DEXA.2011.78. Google Scholar
Digital Library
- Bennett, P. N. and Nguyen, N. 2009. Refined experts: improving classification in large taxonomies. In Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'09). ACM, New York, NY, 11--18. http://dx.doi.org/10.1145/1571941.1571946. Google Scholar
Digital Library
- Bennett, P. N., Svore, K., and Dumais, S. T. 2010. Classification-enhanced ranking. In Proceedings of the 19th International Conference on World Wide Web (WWW'10). ACM, New York, NY, 111--120. http://dx.doi.org/10.1145/1772690.1772703. Google Scholar
Digital Library
- Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30, 1, 107--117. http://dx.doi.org/10.1016/S0169-7552(98)00110-X. Google Scholar
Digital Library
- Broder, A., Ciaramita, M., Fontoura, M., Gabrilovich, E., Josifovski, V., Metzler, D., Murdock, V., and Plachouras, V. 2008. To swing or not to swing: Learning when (not) to advertise. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM'08). ACM, New York, NY, 1003--1012. http://dx.doi.org/10.1145/1458082.1458216. Google Scholar
Digital Library
- Broder, A., Fontoura, M., Gabrilovich, E., Joshi, A., Josifovski, V., and Zhang, T. 2007a. Robust classification of rare queries using Web knowledge. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). ACM, New York, NY, 231--238. http://dx.doi.org/10.1145/1277741.1277783. Google Scholar
Digital Library
- Broder, A., Fontoura, M., Josifovski, V., and Riedel, L. 2007b. A semantic approach to contextual advertising. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). ACM, New York, NY, 559--566. http://dx.doi.org/10.1145/1277741.1277837. Google Scholar
Digital Library
- Chakrabarti, D., Agarwal, D., and Josifovski, V. 2008. Contextual advertising by combining relevance with click feedback. In Proceedings of the 17th International Conference on World Wide Web (WWW'08). ACM, New York, NY, 417--426. http://dx.doi.org/10.1145/1367497.1367554. Google Scholar
Digital Library
- Chatterjee, P., Hoffman, D. L., and Novak, T. P. 2003. Modeling the clickstream: Implications for Web-based advertising efforts. Market. Sci. 22, 4, 520--541. http://dx.doi.org/10.1287/mksc.22.4.520.24906. Google Scholar
Digital Library
- Chen, Y., Xue, G.-R., and Yu, Y. 2008. Advertising keyword suggestion based on concept hierarchy. In Proceedings of the International Conference on Web Search and Data Mining (WSDM'08). ACM, New York, NY, 251--260. http://dx.doi.org/10.1145/1341531.1341564. Google Scholar
Digital Library
- Chirita, P. A., Nejdl, W., Paiu, R., and Kohlschütter, C. 2005. Using ODP metadata to personalize search. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'05). ACM, New York, NY, 178--185. http://dx.doi.org/10.1145/1076034.1076067. Google Scholar
Digital Library
- Craswell, N. and Szummer, M. 2007. Random walks on the click graph. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). ACM, New York, NY, 239--246. http://dx.doi.org/10.1145/1277741.1277784. Google Scholar
Digital Library
- Gabrilovich, E. and Markovitch, S. 2005. Feature generation for text categorization using world knowledge. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05). Morgan Kaufmann, Edinburgh, U.K., 1048--1053. Google Scholar
Digital Library
- Gao, S., Wu, W., Lee, C.-H., and Chua, T.-S. 2006. A maximal figure-of-merit (MFoM)-learning approach to robust classifier design for text categorization. ACM Trans. Inf. Syst. 24, 2, 190--218. http://dx.doi.org/10.1145/1148020.1148022. Google Scholar
Digital Library
- Ha, J., Lee, J.-H., Shim, K.-S., and Lee, S. 2010. EUI: An embedded engine for understanding user intents from mobile devices. In Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM'10). ACM, New York, NY, 1935--1936. http://dx.doi.org/10.1145/1871437.1871771. Google Scholar
Digital Library
- Han, E.-H. (Sam) and Karypis, G. 2000. Centroid-based document classification: Analysis and experimental results. In Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science, Vol. 1910. Springer, Berlin Heidelberg, 424--431. http://dx.doi.org/10.1007/3-540-45372-5_46. Google Scholar
Digital Library
- Haveliwala, T. H. 2002. Topic-sensitive PageRank. In Proceedings of the 11th International Conference on World Wide Web (WWW'02). ACM, New York, NY, 517--526. http://dx.doi.org/10.1145/511446.511513. Google Scholar
Digital Library
- Jeh, G. and Widom, J. 2003. Scaling personalized Web search. In Proceedings of the 12th International Conference on World Wide Web (WWW'03). ACM, New York, NY, 271--279. http://dx.doi.org/10.1145/775152.775191. Google Scholar
Digital Library
- Jin, X., Li, Y., Mah, T., and Tong, J. 2007. Sensitive webpage classification for content advertising. In Proceedings of the 1st International Workshop on Data Mining and Audience Intelligence for Advertising (ADKDD'07). ACM, New York, NY, 28--33. http://dx.doi.org/10.1145/1348599.1348604. Google Scholar
Digital Library
- Joachims, T. 2002. Optimizing search engines using clickthrough data. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'02). ACM, New York, NY, 133--142. http://dx.doi.org/10.1145/775047.775067. Google Scholar
Digital Library
- Lacerda, A., Cristo, M., Gonçalves, M. A., Fan, W., Ziviani, N., and Ribeiro-Neto, B. 2006. Learning to advertise. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'06). ACM, New York, NY, 549--556. http://dx.doi.org/10.1145/1148170.1148265. Google Scholar
Digital Library
- Lee, J.-J., Lee, J.-H., Ha, J., and Lee, S. 2009. Novel webpage classification techniques in contextual advertising. In Proceedings of the 11th International Workshop on Web Information and Data Management (WIDM'09). ACM, New York, NY, 39--47. http://dx.doi.org/10.1145/1651587.1651598. Google Scholar
Digital Library
- Liu, H. and Singh, P. 2004. ConceptNet: A practical commonsense reasoning toolkit. BT Technol. J. 22, 4, 211--226. http://dx.doi.org/10.1023/B:BTTJ.0000047600.45421.6d. Google Scholar
Digital Library
- Liu, T.-Y., Yang, Y., Wan, H., Zeng, H.-J., Chen, Z., and Ma, W.-Y. 2005. Support vector machines classification with a very large-scale taxonomy. SIGKDD Explor. Newsl. 7, 1, 36--43. http://dx.doi.org/10.1145/1089815.1089821. Google Scholar
Digital Library
- Manning, C. D., Raghavan, P., and Schütze, H. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY. Google Scholar
Digital Library
- McCallum, A., Rosenfeld, R., Mitchell, T. M., and Ng, A. Y. 1998. Improving text classification by shrinkage in a hierarchy of classes. In Proceedings of the 15th International Conference on Machine Learning (ICML'98). Morgan Kaufmann Publishers Inc., San Francisco, CA, 359--367. http://dl.acm.org/citation.cfm?id=645527.657461. Google Scholar
Digital Library
- Mei, T., Yang, B., Hua, X.-S., and Li, S. 2011. Contextual video recommendation by multimodal relevance and user feedback. ACM Trans. Inf. Syst. 29, 2, Article 10, 24 pages. http://dx.doi.org/10.1145/1961209.1961213. Google Scholar
Digital Library
- Murdock, V., Ciaramita, M., and Plachouras, V. 2008. Semantic associations for contextual advertising. Int. J. Electron. Commerce Res. Special Issue on Online Advertising and Sponsored Search 9, 1.Google Scholar
- Murdock, V., Ciaramita, M., and Plachouras, V. 2007. A noisy-channel approach to contextual advertising. In Proceedings of the 1st International Workshop on Data Mining and Audience Intelligence for Advertising (ADKDD'07). ACM, New York, NY, 21--27. http://dx.doi.org/10.1145/1348599.1348603. Google Scholar
Digital Library
- ODP. 2008. The open directory project. http://www.dmoz.org.Google Scholar
- Pandey, S., Broder, A., Chierichetti, F., Josifovski, V., Kumar, R., and Vassilvitskii, S. 2009. Nearest-neighbor caching for content-match applications. In Proceedings of the 18th International Conference on World Wide Web (WWW'09). ACM, New York, NY, 441--450. http://dx.doi.org/10.1145/1526709.1526769. Google Scholar
Digital Library
- Ribeiro-Neto, B., Cristo, M., Golgher, P. B., and de Moura, E. S. 2005. Impedance coupling in content-targeted advertising. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'05). ACM, New York, NY, 496--503. http://dx.doi.org/10.1145/1076034.1076119. Google Scholar
Digital Library
- Rocchio, J. J. 1971. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, Salton, G. M. (Ed.). Prentice-Hall, Englewood, Cliffs, NJ, 313--323.Google Scholar
- Salton, G. M., Wong, A., and Yang, C. 1975. A vector space model for automatic indexing. Commun. ACM 18, 11, 613--620. http://dx.doi.org/10.1145/361219.361220. Google Scholar
Digital Library
- Silla, C. N., Jr. and Freitas, A. A. 2011. A survey of hierarchical classification across different application domains. In Data Mining and Knowledge Discovery 22, 1--2, 31--72. http://dx.doi.org/10.1007/s10618-010-0175-9. Google Scholar
Digital Library
- Sinka, M. and Corne, D. 2002. A large benchmark dataset for Web document clustering. In Frontiers in Artificial Intelligence and Applications, Soft Computing Systems: Design, Management and Applications, Vol. 87. 881--890.Google Scholar
- Wang, C., Zhang, P., Choi, R., and D'eredita, M. 2002. Understanding consumers attitude toward advertising. In Proceedings of the 8th Americas Conference on Information Systems (AMCIS'02). 1143--1148.Google Scholar
- Xue, G.-R., Xing, D., Yang, Q., and Yu, Y. 2008. Deep classification in large-scale text hierarchies. In Proceedings of the 31st Annual Internationalc ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). ACM, New York, NY, 619--626. http://dx.doi.org/10.1145/1390334.1390440. Google Scholar
Digital Library
- Yang, Y. 1999. An evaluation of statistical approaches to text categorization. Inf. Retriev. 1, 1--2, 69--90. http://dx.doi.org/10.1023/A:1009982220290. Google Scholar
Digital Library
- Yih, W.-T., Goodman, J., and Carvalho, V. R. 2006. Finding advertising keywords on webpages. In Proceedings of the 15th International Conference on World Wide Web (WWW'06). ACM, New York, NY, 213--222.http://dx.doi.org/10.1145/1135777.1135813. Google Scholar
Digital Library
Index Terms
Semantic contextual advertising based on the open directory project
Recommendations
A semantic approach to contextual advertising
SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrievalContextual 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, ...
Matching and Ranking with Hidden Topics towards Online Contextual Advertising
WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01In online contextual advertising, ad messages are displayed related to the content of the target Web page. It leads to the problem in information retrieval community: how to select the most relevant ad messages given the content of a page. To deal with ...
Studying the Impact of Text Summarization on Contextual Advertising
DEXA '11: Proceedings of the 2011 22nd International Workshop on Database and Expert Systems ApplicationsWeb advertising, one of the major sources of income for a large number of Web sites, is aimed at suggesting products and services to the ever growing population of Internet users. A significant part of Web advertising consists of textual ads, the ...






Comments