short-paper

Fresh BrowseRank

Authors:

Maxim Zhukovskiy,

Andrei Khropov,

Pavel SerdyukovAuthors Info & Claims

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Pages 1029 - 1032

https://doi.org/10.1145/2484028.2484186

Published: 28 July 2013 Publication History

Abstract

In the last years, a lot of attention was attracted by the problem of page authority computation based on user browsing behavior. However, the proposed methods have a number of limitations. In particular, they run on a single snapshot of a user browsing graph ignoring substantially dynamic nature of user browsing activity, which makes such methods recency unaware. This paper proposes a new method for computing page importance, referred to as Fresh BrowseRank. The score of a page by our algorithm equals to the weight in a stationary distribution of a flexible random walk, which is controlled by recency-sensitive weights of vertices and edges. Our method generalizes some previous approaches, provides better capability for capturing the dynamics of the Web and users behavior, and overcomes essential limitations of BrowseRank. The experimental results demonstrate that our method enables to achieve more relevant and fresh ranking results than the classic BrowseRank.

References

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Digital Library

Cited By

Bogolubsky LGusev GRaigorodskii ATikhonov AZhukovskii MDvurechensky PGasnikov ANesterov Y(2016)Learning Supervised PageRank with gradient-based and gradient-free optimization methodsProceedings of the 30th International Conference on Neural Information Processing Systems10.5555/3157382.3157646(4914-4922)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.5555/3157382.3157646
Zhukovskiy MGusev GSerdyukov PLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Supervised Nested PageRankProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661969(1059-1068)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2661969
Yu CLi RYao DLu FJin H(2014)Temporal-Based Ranking in Heterogeneous NetworksAdvanced Information Systems Engineering10.1007/978-3-662-44917-2_3(23-34)Online publication date: 2014
https://doi.org/10.1007/978-3-662-44917-2_3

Index Terms

Fresh BrowseRank
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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: 28 July 2013

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SIGIR '13

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SIGIR

SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

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

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Cited By

Bogolubsky LGusev GRaigorodskii ATikhonov AZhukovskii MDvurechensky PGasnikov ANesterov Y(2016)Learning Supervised PageRank with gradient-based and gradient-free optimization methodsProceedings of the 30th International Conference on Neural Information Processing Systems10.5555/3157382.3157646(4914-4922)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.5555/3157382.3157646
Zhukovskiy MGusev GSerdyukov PLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Supervised Nested PageRankProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661969(1059-1068)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2661969
Yu CLi RYao DLu FJin H(2014)Temporal-Based Ranking in Heterogeneous NetworksAdvanced Information Systems Engineering10.1007/978-3-662-44917-2_3(23-34)Online publication date: 2014
https://doi.org/10.1007/978-3-662-44917-2_3

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