MFCRank: A Web ranking algorithm based on correlation of multiple features

Yunming Ye; Yan Li; Xiaofei Xu; Joshua Huang; Xiaojun Chen

doi:10.1007/11671299_39

MFCRank: A Web ranking algorithm based on correlation of multiple features

Yunming Ye^*
, Yan Li
, Xiaofei Xu
, Joshua Huang
, Xiaojun Chen

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
The University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper presents a new ranking algorithm MFCRank for topic-specific Web search systems. The basic idea is to correlate two types of similarity information into a unified link analysis model so that the rich content and link features in Web collections can be exploited efficiently to improve the ranking performance. First, a new surfer model JBC is proposed, under which the topic similarity information among neighborhood pages is used to weigh the jumping probability of the surfer and to direct the surfing activities. Secondly, as JBC surfer model is still query-independent, a correlation between the query and JBC is essential. This is implemented by the definition of MFCRank score, which is the linear combination of JBC score and the similarity value between the query and the matched pages. Through the two correlation steps, the features contained in the plain text, link structure, anchor text and user query can be smoothly correlated in one single ranking model. Ranking experiments have been carried out on a set of topic-specific Web page collections. Experimental results showed that our algorithm gained great improvement with regard to the ranking precision.

Original language	English
Title of host publication	Computational Linguistics and Intelligent Text Processing - 7th International Conference, CICLing 2006, Proceedings
Publisher	Springer Verlag
Pages	378-388
Number of pages	11
ISBN (Print)	3540322051, 9783540322054
DOIs	https://doi.org/10.1007/11671299_39
State	Published - 2006
Externally published	Yes
Event	7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006 - Mexico City, Mexico Duration: 19 Feb 2006 → 25 Feb 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3878 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006
Country/Territory	Mexico
City	Mexico City
Period	19/02/06 → 25/02/06

Keywords

Link Analysis
PageRank
Ranking
Search Engine
Web

Access to Document

10.1007/11671299_39

Cite this

Ye, Y., Li, Y., Xu, X., Huang, J., & Chen, X. (2006). MFCRank: A Web ranking algorithm based on correlation of multiple features. In Computational Linguistics and Intelligent Text Processing - 7th International Conference, CICLing 2006, Proceedings (pp. 378-388). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3878 LNCS). Springer Verlag. https://doi.org/10.1007/11671299_39

Ye, Yunming ; Li, Yan ; Xu, Xiaofei et al. / MFCRank : A Web ranking algorithm based on correlation of multiple features. Computational Linguistics and Intelligent Text Processing - 7th International Conference, CICLing 2006, Proceedings. Springer Verlag, 2006. pp. 378-388 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "MFCRank: A Web ranking algorithm based on correlation of multiple features",

abstract = "This paper presents a new ranking algorithm MFCRank for topic-specific Web search systems. The basic idea is to correlate two types of similarity information into a unified link analysis model so that the rich content and link features in Web collections can be exploited efficiently to improve the ranking performance. First, a new surfer model JBC is proposed, under which the topic similarity information among neighborhood pages is used to weigh the jumping probability of the surfer and to direct the surfing activities. Secondly, as JBC surfer model is still query-independent, a correlation between the query and JBC is essential. This is implemented by the definition of MFCRank score, which is the linear combination of JBC score and the similarity value between the query and the matched pages. Through the two correlation steps, the features contained in the plain text, link structure, anchor text and user query can be smoothly correlated in one single ranking model. Ranking experiments have been carried out on a set of topic-specific Web page collections. Experimental results showed that our algorithm gained great improvement with regard to the ranking precision.",

keywords = "Link Analysis, PageRank, Ranking, Search Engine, Web",

author = "Yunming Ye and Yan Li and Xiaofei Xu and Joshua Huang and Xiaojun Chen",

year = "2006",

doi = "10.1007/11671299\_39",

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isbn = "3540322051",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Ye, Y, Li, Y, Xu, X, Huang, J & Chen, X 2006, MFCRank: A Web ranking algorithm based on correlation of multiple features. in Computational Linguistics and Intelligent Text Processing - 7th International Conference, CICLing 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3878 LNCS, Springer Verlag, pp. 378-388, 7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006, Mexico City, Mexico, 19/02/06. https://doi.org/10.1007/11671299_39

MFCRank: A Web ranking algorithm based on correlation of multiple features. / Ye, Yunming; Li, Yan; Xu, Xiaofei et al.
Computational Linguistics and Intelligent Text Processing - 7th International Conference, CICLing 2006, Proceedings. Springer Verlag, 2006. p. 378-388 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3878 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T2 - 7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006

AU - Ye, Yunming

AU - Li, Yan

AU - Xu, Xiaofei

AU - Huang, Joshua

AU - Chen, Xiaojun

PY - 2006

Y1 - 2006

N2 - This paper presents a new ranking algorithm MFCRank for topic-specific Web search systems. The basic idea is to correlate two types of similarity information into a unified link analysis model so that the rich content and link features in Web collections can be exploited efficiently to improve the ranking performance. First, a new surfer model JBC is proposed, under which the topic similarity information among neighborhood pages is used to weigh the jumping probability of the surfer and to direct the surfing activities. Secondly, as JBC surfer model is still query-independent, a correlation between the query and JBC is essential. This is implemented by the definition of MFCRank score, which is the linear combination of JBC score and the similarity value between the query and the matched pages. Through the two correlation steps, the features contained in the plain text, link structure, anchor text and user query can be smoothly correlated in one single ranking model. Ranking experiments have been carried out on a set of topic-specific Web page collections. Experimental results showed that our algorithm gained great improvement with regard to the ranking precision.

AB - This paper presents a new ranking algorithm MFCRank for topic-specific Web search systems. The basic idea is to correlate two types of similarity information into a unified link analysis model so that the rich content and link features in Web collections can be exploited efficiently to improve the ranking performance. First, a new surfer model JBC is proposed, under which the topic similarity information among neighborhood pages is used to weigh the jumping probability of the surfer and to direct the surfing activities. Secondly, as JBC surfer model is still query-independent, a correlation between the query and JBC is essential. This is implemented by the definition of MFCRank score, which is the linear combination of JBC score and the similarity value between the query and the matched pages. Through the two correlation steps, the features contained in the plain text, link structure, anchor text and user query can be smoothly correlated in one single ranking model. Ranking experiments have been carried out on a set of topic-specific Web page collections. Experimental results showed that our algorithm gained great improvement with regard to the ranking precision.

KW - Link Analysis

KW - PageRank

KW - Ranking

KW - Search Engine

KW - Web

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SN - 9783540322054

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 378

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BT - Computational Linguistics and Intelligent Text Processing - 7th International Conference, CICLing 2006, Proceedings

PB - Springer Verlag

Y2 - 19 February 2006 through 25 February 2006

ER -

Ye Y, Li Y, Xu X, Huang J, Chen X. MFCRank: A Web ranking algorithm based on correlation of multiple features. In Computational Linguistics and Intelligent Text Processing - 7th International Conference, CICLing 2006, Proceedings. Springer Verlag. 2006. p. 378-388. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11671299_39

MFCRank: A Web ranking algorithm based on correlation of multiple features

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