Abstract
Anchor text has been considered as a useful resource to complement the representation of target pages and is broadly used in web search. However, previous research only uses anchor text of a single snapshot to improve web search. Historical trends of anchor text importance have not been well modeled in anchor text weighting strategies. In this paper, we propose a novel temporal anchor text weighting method to incorporate the trends of anchor text creation over time, which combines historical weights of anchor text by propagating the anchor text weights among snapshots over the time axis. We evaluate our method on a real-world web crawl from the Stanford WebBase. Our results demonstrate that the proposed method can produce a significant improvement in ranking quality.
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References
Acharya, A., Cutts, M., Dean, J., Haahr, P., Henzinger, M., Hoelzle, U., Lawrence, S., Pfleger, K., Sercinoglu, O., Tong, S.: Information retrieval based on historical data. US Patent 7346839 (March 2008)
Berberich, K., Vazirgiannis, M., Weikum, G.: Time-aware authority ranking. In: Leonardi, S. (ed.) WAW 2004. LNCS, vol. 3243, pp. 131–142. Springer, Heidelberg (2004)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: Proc. of the 7th Int’l. World Wide Web Conf. (April 1998)
Cho, J., Garcia-Molina, H., Haveliwala, T., Lam, W., Paepcke, A., Raghavan, S., Wesley, G.: Stanford WebBase components and applications. ACM Trans. on Internet Technology 6(2), 153–186 (2006)
Dakka, W., Gravano, L., Ipeirotis, P.G.: Answering general time sensitive queries. In: Proc. of the 17th ACM Conf. on Information and Knowledge Management (CIKM), pp. 1437–1438. ACM, New York (2008)
de Kretser, O., Moffat, A.: Effective document presentation with a locality-based similarity heuristic. In: Proc. 22nd Annual Int’l ACM SIGIR Conf. on Research and Dev. in Information Retrieval (July 1999)
Dou, Z., Song, R., Nie, J.-Y., Wen, J.-R.: Using anchor texts with their hyperlink structure for web search. In: Proc. 32nd Annual Int’l. ACM SIGIR Conf. on Research and Dev. in Information Retrieval (July 2009)
Eiron, N., McCurley, K.S.: Analysis of anchor text for web search. In: Proc. 26th Annual Intl. ACM SIGIR Conf. on Research and Dev. in Information Retrieval (July 2003)
Fujii, A.: Modeling anchor text and classifying queries to enhance web document retrieval. In: Proc. of the 17th Int’l. World Wide Web Conf. (April 2008)
Internet Archive. The Internet Archive (2009), http://www.archive.org/
Jarvelin, K., Kekalainen, J.: IR evaluation methods for retrieving highly relevant documents. In: Proc. of the 23rd Annual Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval, July 2000, pp. 41–48 (2000)
Jones, R., Diaz, F.: Temporal profiles of queries. ACM Trans. Inf. Syst. 25(3), 14 (2007)
Kise, K., Junker, M., Dengel, A., Matsumoto, K.: Passage retrieval based on density distributions of terms and its applications to document retrieval and question answering. In: Dengel, A.R., Junker, M., Weisbecker, A. (eds.) Reading and Learning. LNCS, vol. 2956, pp. 306–327. Springer, Heidelberg (2004)
Kraft, R., Zien, J.: Mining anchor text for query refinement. In: Proc. of the 13th Int’l World Wide Web Conf., pp. 666–674. ACM, New York (2004)
Lee, U., Liu, Z., Cho, J.: Automatic identification of user goals in web search. In: Proc. of the 14th Int’l World Wide Web Conf., pp. 391–400. ACM Press, New York (2005)
Li, X., Croft, W.B.: Time-based language models. In: Proc. of the 20th Int’l. Conf. on Information and knowledge management, pp. 469–475. ACM, New York (2003)
Lu, W.-H., Chien, L.-F., Lee, H.-J.: Anchor text mining for translation of web queries: A transitive translation approach. ACM Trans. Inf. Syst. 22(2), 242–269 (2004)
Lv, Y., Zhai, C.: Positional language models for information retrieval. In: Proc. 32nd Annual Int’l. ACM SIGIR Conf. on Research and Dev. in Information Retrieval (July 2009)
Metzler, D., Novak, J., Cui, H., Reddy, S.: Building enriched document representations using aggregated anchor text. In: Proc. 32nd Annual Int’l ACM SIGIR Conf. on Research and Dev. in Information Retrieval (July 2009)
NIST. Text REtrieval Conference (TREC) home page (2008), http://trec.nist.gov/
Petkova, D., Croft, W.B.: Proximity-based document representation for named entity retrieval. In: Proc. of the 16th ACM Conf. on information and knowledge management, pp. 731–740. ACM, New York (2007)
Rao, S.S.: On multiple regression models with nonstationary correlated errors. Biometrika 91(3), 645–659 (2004)
Robertson, S., Zaragoza, H., Taylor, M.: Simple bm25 extension to multiple weighted fields. In: Proc. of the 13th ACM Int’l. Conf. on Information and knowledge management (CIKM), pp. 42–49. ACM, New York (2004)
Zaragoza, H., Craswell, N., Taylor, M., Saria, S., Robertson, S.: Microsoft cambridge at trec 13: Web and hard tracks. In: TREC ’13: Proceedings of the thirteenth Text REtrieval Conference (2004)
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Dai, N., Davison, B.D. (2010). Mining Anchor Text Trends for Retrieval. In: Gurrin, C., et al. Advances in Information Retrieval. ECIR 2010. Lecture Notes in Computer Science, vol 5993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12275-0_14
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DOI: https://doi.org/10.1007/978-3-642-12275-0_14
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