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Conference Papers Year : 2011

A Framework for Web Page Rank Prediction

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Abstract

We propose a framework for predicting the ranking position of a Web page based on previous rankings. Assuming a set of successive top-k rankings, we learn predictors based on different methodologies.The prediction quality is quantified as the similarity between the predicted and the actual rankings. Extensive experiments were performed on real world large scale datasets for global and query-based top-k rankings, using a variety of existing similarity measures for comparing top-k ranked lists, including a novel and more strict measure introduced in this paper. The predictions are highly accurate and robust for all experimental setups and similarity measures.
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Dates and versions

hal-01571452 , version 1 (02-08-2017)

Licence

Attribution - CC BY 4.0

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Elli Voudigari, John Pavlopoulos, Michalis Vazirgiannis. A Framework for Web Page Rank Prediction. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.240-249, ⟨10.1007/978-3-642-23960-1_29⟩. ⟨hal-01571452⟩
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