%0 Conference Proceedings
%T A Framework for Web Page Rank Prediction
%+ Athens University of Economics and Business (AUEB)
%+ Télécom ParisTech
%A Voudigari, Elli
%A Pavlopoulos, John
%A Vazirgiannis, Michalis
%Z Part 11: Web Applications of ANN
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI)
%C Corfu, Greece
%Y Lazaros Iliadis
%Y Ilias Maglogiannis
%Y Harris Papadopoulos
%I Springer
%3 Artificial Intelligence Applications and Innovations
%V AICT-364
%N Part II
%P 240-249
%8 2011-09-15
%D 2011
%R 10.1007/978-3-642-23960-1_29
%K Rank Prediction
%K Data Mining
%K Web Mining
%K Artificial Intelligence
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-01571452/document
%2 https://inria.hal.science/hal-01571452/file/978-3-642-23960-1_29_Chapter.pdf
%L hal-01571452
%U https://inria.hal.science/hal-01571452
%~ INSTITUT-TELECOM
%~ ENST
%~ TELECOM-PARISTECH
%~ PARISTECH
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-EANN
%~ IFIP-AICT-364