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Advertising keyword suggestion based on concept hierarchy

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Published:11 February 2008Publication History

ABSTRACT

The increasing growth of the World Wide Web constantly enlarges the revenue generated by search engine advertising. Advertisers bid on keywords associated with their products to display their ads on the search result pages. Keyword suggestion methods are proposed to fill the gap between the keywords chosen by advertisers and the popular queries, through finding new relevant keywords according to some statistical information (for example, the keyword co-occurrence). However, there is little effort taking semantic information, such as concept hierarchy, into account. In this paper, we propose a novel keyword suggestion method that fully exploits the semantic knowledge among concept hierarchy. Given a keyword, we first match it with some relevant concepts. Then the relevant concepts are used with their hierarchy to fertilize the meanings of the keywords. Finally new keywords are suggested according to the concept information rather than the statistical co-occurrence of the keyword itself. Experimental results show that our proposed method can successfully provide suggestion that meets the accuracy and coverage requirements

References

  1. Abhishek, V. Keyword Generation for Search Engine Advertising using Semantic Similarity between Terms. Workshop on Sponsored Search Auctions, WWW (2007).Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Baeza-Yates, R. A. and Ribeiro-Neto, B. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bartz, K., Murthi, V. and Sebastian, S. Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation. Proceedings of the Second Workshop on Sponsored Search Auctions (2006).Google ScholarGoogle Scholar
  4. Brown, P. F., Della Pietra, V. J., deSouza, P. V. and Lai, J. C. Class-based ngram models of natural language. Computational Linguistics, 18(1992). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Buckley, C., Salton, G., Allan, J. and Singhal, A. Automatic Query Expansion Using SMART: TREC 3. Overview of the Third Text REtrieval Conference (TREC-3) (1994).Google ScholarGoogle Scholar
  6. Carrasco, J., Fain, D., Lang, K. and Zhukov, L. Clustering of bipartite advertiser-keyword graph. International Conference on Data Mining (2003).Google ScholarGoogle Scholar
  7. Cimiano, P., Pivk, A., Schmidt-Thieme, L. and Staab, S. Learning taxonomic relations from heterogeneous sources. Proceedings of the ECAI 2004 Ontology Learning and Population Workshop (2004).Google ScholarGoogle Scholar
  8. Doan-Nguyen, H. and Kosseim, L. Using Terminology and a Concept Hierarchy for Restricted-Domain Question-Answering. Research on Computing Science, Special issue on Advances in Natural Language Processing, 18(2006).Google ScholarGoogle Scholar
  9. Ganesan, P., Garcia-Molina, H. and Widom, J. Exploiting hierarchical domain structure to compute similarity. ACM Trans. Inf. Syst. 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Gelbukh, A. F., Sidorov, G. and Guzman-Arenas, A. Document Indexing With a Concept Hierarchy. New Developments in Digital Libraries, Proceedings of the 1st International Workshop on New Developments in Digital Libraries (2001). Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Google AdWords Keyword Tool, https://adwords.google.com/select/KeywordToolExternalGoogle ScholarGoogle Scholar
  12. Huang, C.-C., Chuang, S.-L. and Chien, L.-F. Liveclassifier: creating hierarchical text classifiers through web corpora. Proceedings of the 13th international conference on World Wide Web (2004). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. JÄarvelin, K. and KekÄalÄainen, J. IR evaluation methods for retrieving highly relevant documents. Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval (2000). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Jones, R., Rey, B., Madani, O. and Greiner, W. Generating query substitutions. Proceedings of the 15th international conference on World Wide Web (2006). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Joshi, A. and Motwani, R. Keyword Generation for Search Engine Advertising. Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Koller, D. and Sahami, M. Hierarchically Classifying Documents Using Very Few Words. Proceedings of the Fourteenth International Conference on Machine Learning (1997). Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Nanas, N., Uren, V. and Roeck, A. D. Building and applying a concept hierarchy representation of a user profile. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Open Directory Project, http://www.dmoz.orgGoogle ScholarGoogle Scholar
  19. Overture Keyword Selection Tool, http://inventory.overture.com/d/searchinventory/suggestion/Google ScholarGoogle Scholar
  20. Qiu, Y. and Frei, H.-P. Concept based query expansion. Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval (1993). Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Ribeiro-Neto, B., Cristo, M., Golgher, P. B. and Moura, E. S. d. Impedance coupling in content-targeted advertising. Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (2005). Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Sanderson, M. and Croft, B. Deriving concept hierarchies from text. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (1999). Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Santamaria, C., Gonzalo, J., Verdejo, F. Automatic Association of Web Directories to Word Senses. Computational Linguistics (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. The Google adwords. Google content-targeted advertising. http://adwords.google.com/select/ct_faq.html, November 2004.Google ScholarGoogle Scholar
  25. Wang, BB., McKay, RI, Abbass, HA, Barlow, M., Learning text classifier using the domain concept hierarchy, IEEE International Conference on Communications (2002).Google ScholarGoogle ScholarCross RefCross Ref
  26. Wang, W., Meng, W. and Yu, C. Concept Hierarchy Based Text Database Categorization in a Metasearch Engine Environment. Proceedings of the First International Conference on Web Information Systems Engineering (2000). Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Wang, K., Zhou, S. and Liew, S. C. Building Hierarchical Classifiers Using Class Proximity. Proceedings of the 25th International Conference on Very Large Data Bases (1999). Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Witten, I. H., Paynter, G. W., Frank, E., Gutwin, C. and Nevill-Manning, C. G. KEA: practical automatic keyphrase extraction. Proceedings of the fourth ACM conference on Digital libraries (1999). Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. WordTracker, http://www.wordtracker.com/Google ScholarGoogle Scholar
  30. Xu, J. and Croft, W. B. Improving the effectiveness of information retrieval with local context analysis. ACM Press, City, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Yih, W.-t., Goodman, J. and Carvalho, V. R. Finding advertising keywords on web pages. Proceedings of the 15th international conference on World Wide Web (2006). Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data Mining
      February 2008
      270 pages
      ISBN:9781595939272
      DOI:10.1145/1341531

      Copyright © 2008 ACM

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      Publication History

      • Published: 11 February 2008

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