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Conference papers

Relevance Propagation Model for Large Hypertext Document Collections

Abstract : Web search engines have become indispensable in our daily life to help us finding the information we need. Several search tools, for instance Google, use links to select the matching documents against a query. In this paper, we propose a new ranking function that combines content and link rank based on propagation of scores over links. This function propagates scores from source pages to destination pages in relation with query terms. We assessed our ranking function with experiments over two test collections WT10g and GOV. We conclude that propagating link scores according to query terms provides significant improvement for information retrieval.
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Contributor : Evelyne Faivre Connect in order to contact the contributor
Submitted on : Friday, February 1, 2008 - 11:12:22 AM
Last modification on : Monday, December 14, 2020 - 12:28:23 PM


  • HAL Id : hal-00232593, version 1



Idir Chibane, Bich-Liên Doan. Relevance Propagation Model for Large Hypertext Document Collections. RIAO 2007, May 2007, Pittsburgh, United States. pp.1-11. ⟨hal-00232593⟩



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