Article

Page quality: in search of an unbiased web ranking

Authors:

Robert E. AdamsAuthors Info & Claims

SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data

Pages 551 - 562

https://doi.org/10.1145/1066157.1066220

Published: 14 June 2005 Publication History

Abstract

In a number of recent studies [4, 8] researchers have found that because search engines repeatedly return currently popular pages at the top of search results, popular pages tend to get even more popular, while unpopular pages get ignored by an average user. This "rich-get-richer" phenomenon is particularly problematic for new and high-quality pages because they may never get a chance to get users' attention, decreasing the overall quality of search results in the long run. In this paper, we propose a new ranking function, called page quality that can alleviate the problem of popularity-based ranking. We first present a formal framework to study the search engine bias by discussing what is an "ideal" way to measure the intrinsic quality of a page. We then compare how PageRank, the current ranking metric used by major search engines, differs from this ideal quality metric. This framework will help us investigate the search engine bias in more concrete terms and provide clear understanding why PageRank is effective in many cases and exactly when it is problematic. We then propose a practical way to estimate the intrinsic page quality to avoid the inherent bias of PageRank. We derive our proposed quality estimator through a careful analysis of a reasonable web user model, and we present experimental results that show the potential of our proposed estimator. We believe that our quality estimator has the potential to alleviate the rich-get-richer phenomenon and help new and high-quality pages get the attention that they deserve.

References

[1]

S. Abiteboul, M. Preda, and G. Cobna. Adaptive on-line page importance computation. In Proceedings of the International World-Wide Web Conference, May 2003.

Digital Library

[2]

D. Achlioptas, A. Fiat, A. R. Karlin, and F. McSherry. Web search via hub synthesis. In IEEE Symposium on Foundations of Computer Science, pages 500--509, 2001.

Digital Library

[3]

R. Albert, A.-L. Barabasi, and H. Jeong. Diameter of the World Wide Web. Nature, 401(6749):130--131, September 1999.

[4]

R. Baeza-Yates, F. Saint-Jean, and C. Castillo. Web dynamics, age and page quality. In Proceedings of SPIRE 2002, September 2002.

Digital Library

[5]

A. Balmin, V. Hristidis, and Y. Papakonstantinou. ObjectRank: authority-based keyword search in databases. In Proceedings of the International Conference on Very Large Databases (VLDB), August 2004.

Digital Library

[6]

A.-L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509--512, October 1999.

[7]

A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener. Graph structure in the web: experiments and models. In Proceedings of the International World-Wide Web Conference, May 2000.

Digital Library

[8]

J. Cho and S. Roy. Impact of search engines on page popularity. In Proceedings of the International World-Wide Web Conference, May 2004.

Digital Library

[9]

J. Cho, S. Roy, and R. E. Adams. Page quality: In search of an unbiased web ranking. Technical report, UCLA Computer Science, 2005.

[10]

N. Fuhr. Probabilistic models in information retrieval. The Computer Journal, 35(3):243--255, 1992.

Digital Library

[11]

F. Geerts, H. Mannila, and E. Terzi. Relational link-based ranking. In Proceedings of the International Conference on Very Large Databases (VLDB), August 2004.

Digital Library

[12]

R. Goldman, N. Shivakumar, S. Venkatasubramanian, and H. Garcia-Molina. Proximity search in databases. In Proceedings of the International Conference on Very Large Databases (VLDB), pages 26--37, 1998.

Digital Library

[13]

S. P. Harter. Variations in relevance assessments and the measurement of retrieval effectiveness. Journal of the American Society for Information Science, 47(1):37--49, December 1996.

Digital Library

[14]

T. H. Haveliwala. Topic-sensitive pagerank. In Proceedings of the International World-Wide Web Conference, May 2002.

Digital Library

[15]

S. Kamvar, T. Haveliwala, and G. Golub. Adaptive methods for the computation of pagerank. In Proceedings of International Conference on the Numerical Solution of Markov Chains. September 2003.

[16]

S. Kamvar, T. Haveliwala, C. Manning, and G. Golub. Extrapolation methods for accelerating pagerank computations. In Proceedings of the International World-Wide Web Conference, May 2003.

Digital Library

[17]

J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, September 1999.

Digital Library

[18]

S. Mizzaro. Measuring the agreement among relevance judges. In Proceedings of MIRA Conference, April 1999.

[19]

Nielsen NetRatings. http://www.nielsen-netratings.com/.

[20]

Npd search and portal site study. Available at http: / /www.npd.com/press/releases/press_000919.htm.

[21]

S. Olsen. Does search engine's power threaten web's independence? Available at http://news.com.com/2009--1023-963618.html, October 2002.

[22]

L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford University Database Group, 1998. Available at http://dbpubs.stanford.edu:8090/pub/1999--66.

[23]

D. M. Pennock, G. W. Flake, S. Lawrence, E. J. Glover, and C. L. Giles. Winners don't take all: Characterizing the competition for links on the web. Proceedings of the National Academy of Sciences, 99(8):5207--5211, 2002.

[24]

S. E. Robertson and K. Sparck-Jones. Relevance weighting of search terms. Journal of the American Society for Information Science, 27(3):129--146, 1975.

[25]

G. Salton. The SMART Retrieval System -- Experiments in Automatic Document Processing. Prentice Hall Inc., 1971.

Digital Library

[26]

G. Salton and M. J. McGill. Introduction to modern information retrieval. McGraw-Hill, 1983.

Digital Library

[27]

J. A. Tomlin. A new paradigm for ranking pages on the world wide web. In Proceedings of the International World-Wide Web Conference, May 2003.

Digital Library

[28]

TREC: Text retrieval conference. http://trec.nist.gov.

[29]

A. C. Tsoi, G. Morini, F. Scarselli, M. Hagenbuchner, and M. Maggini. Adaptive ranking of web pages. In Proceedings of the International World-Wide Web Conference, May 2003.

Digital Library

[30]

F. Verhulst. Nonlinear Differential Equations and Dynamical Systems, Springer Verlag, 2nd edition, 1997.

Digital Library

[31]

Y. Wang and D. DeWitt. Computing pagerank in a distributed internet search system. In Proceedings of the International Conference on Very Large Databases (VLDB), August 2004.

Digital Library

[32]

S. Wartick. Boolean operations. Information Retrieval: Data Structures and Algorithms, pages 264--292, 1992.

Digital Library

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cover image ACM Conferences

SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data

June 2005

990 pages

ISBN:1595930604

DOI:10.1145/1066157

Conference Chair:
Fatma Ozcan
IBM Almaden Research Center

Copyright © 2005 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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Published: 14 June 2005

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SIGMOD/PODS05: International Conference on Management of Data and Symposium on Principles Database and Systems

June 14 - 16, 2005

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Yu SGong SZhang YYu WYin QTian CTao QYan YYu GZhou J(2023)Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00212(2766-2779)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00212
Ku SKim SYou MWhitaker M(2023)Building the Groundwork for a Natural Search, to Make Accurate and Trustworthy Filtered Searches: The Case of a New Educational Platform with a Global Heat Map to Geolocate Innovations in Renewable EnergyIntelligent Human Computer Interaction10.1007/978-3-031-27199-1_25(239-250)Online publication date: 11-Apr-2023
https://doi.org/10.1007/978-3-031-27199-1_25
Ali FKhusro S(2021)Content and link-structure perspective of ranking webpagesComputer Science Review10.1016/j.cosrev.2021.10039740:COnline publication date: 1-May-2021
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100397
Balayn ALofi CHouben G(2021)Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systemsThe VLDB Journal10.1007/s00778-021-00671-830:5(739-768)Online publication date: 5-May-2021
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Sankpal LPatil S(2020)Rider-Rank Algorithm-Based Feature Extraction for Re-ranking the Webpages in the Search EngineThe Computer Journal10.1093/comjnl/bxaa032Online publication date: 12-Jun-2020
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Kyriakakis AKoumakis LKanterakis AIatraki GTsiknakis MPotamias G(2019)Enabling Ontology-Based Search: A Case Study in the Bioinformatics Domain2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)10.1109/BIBE.2019.00048(227-234)Online publication date: Oct-2019
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Chaudhary KGupta NKumar S(2018)Web Documents Prioritization Using Iterative ImprovementSmart and Innovative Trends in Next Generation Computing Technologies10.1007/978-981-10-8657-1_35(459-473)Online publication date: 9-Jun-2018
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