Abstract
Even though search engines cover billions of pages and perform quite well, it is still difficult to find the right information from the returned results. In this paper we present a system that allows a user to re-rank the results locally by augmenting a query with positive example pages. Since it is not always easy to come up with many example pages, our system aims to work with only a couple of positive training examples and without any negative ones. Our approach creates artificial (virtual) negative examples based upon the returned pages and the example pages before the training commences. The list of results is then re-ordered according to the outcome from the machine learner. We have further shown that our system performs sufficiently well even if the example pages belong to a slightly different (but related) domain.
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References
Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Cotraining. In: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann Publishers, San Francisco (1998)
Brin, S., Page, L.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. In: Proc. of 1998 WWW Conference (1998)
Butler, D.: Souped-up search engines. Nature 405, 112–115 (2000)
Chau, M., Zeng, D., Chen, H.: Personalized Spiders for Web Search and Analysis. In: Proceedings of the First ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL 2001), Roanoke, Virginia, June 24-28, pp. 79–87 (2001)
Etzioni, O.: Moving Up the Information Food Chain: Deploying Softbots on the World Wide Web (1996)
Glover, E.J., Flake, G.W., Lawrence, S., Birmingham, W.P., Kruger, A., Giles, C.L., Pennock, D.M.: Improving Category Specific Web Search by Learning Query Modifications. In: Symposium on Applications and the Internet, SAINT 2001, San Diego, California, January 8–12 (2001)
Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)
Kruschwitz, U.: A Rapidly Acquired Domain Model Derived from Markup Structure. In: Proceedings of the ESSLLI 2001 Workshop on Semantic Knowledge Acquisition and Categorisation, Helsinki (2001)
Oyama, S., Kokubo, T., Ishida, T.: Domain-Specific Web Search with Keyword Spices. IEEE Transactions on Knowledge and Data Engineering (2003) (to appear)
Schölkopf, B., Burges, C., Vapnik, V.: Incorporating Invariances in Support Vector Learning Machines. In: Vorbrüggen, J.C., von Seelen, W., Sendhoff, B. (eds.) ICANN 1996. LNCS, vol. 1112, Springer, Heidelberg (1996)
Sullivan, D.: Search Engine Sizes, September 2 (2003), http://searchenginewatch.com/reports/article.php/2156481
Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Widyantoro, D.H., Yen, J.: A Fuzzy Ontology-based Abstract Search Engine and Its User Studies. In: Proceedings of the 10th IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1291–1294 (2001)
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Buchholz, M., Pflüger, D., Poon, J. (2004). Application of Machine Learning Techniques to the Re-ranking of Search Results. In: Biundo, S., Frühwirth, T., Palm, G. (eds) KI 2004: Advances in Artificial Intelligence. KI 2004. Lecture Notes in Computer Science(), vol 3238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30221-6_7
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DOI: https://doi.org/10.1007/978-3-540-30221-6_7
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