research-article

Local Ranking Problem on the BrowseGraph

Authors:

Michele Trevisiol,

Luca Maria Aiello,

Roi BlancoAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 173 - 182

https://doi.org/10.1145/2766462.2767704

Published: 09 August 2015 Publication History

Abstract

The "Local Ranking Problem" (LRP) is related to the computation of a centrality-like rank on a local graph, where the scores of the nodes could significantly differ from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many different tasks such as ranking, prediction and recommendation. However, a web-server has only the browsing traffic performed on its pages (local BrowseGraph) and, as a consequence, the local computation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly overlooked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user browsing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from different domains. We show that the distance between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8.

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Cited By

Ren YTomko MSalim FChan JClarke CSanderson M(2018)A Location-Query-Browse Graph for Contextual RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.276605930:2(204-218)Online publication date: 1-Feb-2018
https://doi.org/10.1109/TKDE.2017.2766059
Lai SShao BXu YLin X(2017)Parallel computations of local PageRank problem based on Graphics Processing UnitConcurrency and Computation: Practice and Experience10.1002/cpe.424529:24Online publication date: 24-Aug-2017
https://doi.org/10.1002/cpe.4245

Index Terms

Local Ranking Problem on the BrowseGraph
1. Information systems
  1. Information systems applications
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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Published In

cover image ACM Conferences

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

Copyright © 2015 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 August 2015

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Ministry of Science and Innovation of Spain
EU-FET NADINE
Yahoo Faculty Research Engagement Program

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SIGIR '15

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SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Ren YTomko MSalim FChan JClarke CSanderson M(2018)A Location-Query-Browse Graph for Contextual RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.276605930:2(204-218)Online publication date: 1-Feb-2018
https://doi.org/10.1109/TKDE.2017.2766059
Lai SShao BXu YLin X(2017)Parallel computations of local PageRank problem based on Graphics Processing UnitConcurrency and Computation: Practice and Experience10.1002/cpe.424529:24Online publication date: 24-Aug-2017
https://doi.org/10.1002/cpe.4245

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