Extending Link-based Algorithms for Similar Web Pages with Neighborhood Structure

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Extending Link-based Algorithms for Similar Web
Pages with Neighborhood Structure

• Allen, Zhenjiang LIN
• CSE, CUHK
• 13 Dec 2006

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Outline

• 1. Introduction
• 2. Extended Neighborhood Structure Model
• 3. Extending Link-based Similarity Measures
• 4. Experimental Results
• 5. Conclusion and Future Work

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1. Introduction

• Background
• Similarity measures are required in many web
  applications to evaluate the similarity between
  web pages.
• The similar pages service of Web search
  engines
• Web document classification
• Web community identification.
• Problem
• Many link-based similarity measures are not so
  accurate since they consider only part of the
  structural information.

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1. Introduction

• Motivation
• How to improve the accuracy of link-based
  similarity measures by making full use of the
  structural information?
• Contributions
• Propose the Extended Neighborhood Structure (ENS)
  model.
• bi-direction
• multi-hop
• Construct extended link-based similarity measures
  base on the ENS model.
• more flexible and accurate

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1. Introduction

• Searching the Web
• Keyword searching
• Similarity searching

KEYWORDS news
http//news.bbc.co.uk/ http//www.cnn.com/
URL www.cnn.com
Search Engine
http//news.bbc.co.uk/ http//usnews.com/
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1. Introduction

• Similarity measures
• Evaluate how similarity or related two objects
  are.
• Approaches to measuring similarity
• Text-based
• Cosine TFIDF Joachims97
• Link-based
• Bibliographic coupling Kessler63
• Co-citation Small73
• SimRank Jeh et al 02, PageSim Lin et al 06
• Hybrid

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2. Extend Neighborhood Structure Model

• Extended Neighborhood Structure (ENS) model
• Question what hide in hyperlinks?
• similarity relationship between pages,
• similarity relationship decrease along hyperlinks.

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2. Extend Neighborhood Structure Model

• Extended Neighborhood Structure (ENS) model
• The ENS model
• bi-direction
• in-link
• out-link
• multi-hop
• direct (1-hop)
• indirect (2-hop, 3-hop, etc)
• Purpose
• Improve accuracy of link-based similarity
  measures by helping them make full use of the
  structural information of the Web.

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3. Extending Link-based Similarity Measures

• Intuition of similarity
• Similar web pages have similar neighbors.(to
  compare two web pages, see their neighbors.)
• Notations
• G(V, E), V n the web graph.
• I(a) / O(a) in-link / out-link neighbors of web
  page a.
• path(a1, as) a sequence of vertices a1, a2, ,
  as such that (ai, ai1) ? E (i1,,s-1) and ai
  are distinct.
• PATH(a,b) the set of all possible paths from
  page a to b.
• Sim(a,b) similarity score of web page a and b.

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3. Extending Link-based Similarity Measures

• Two classical methods
• Co-citation the more common in-link neighbors,
  the more similar.
• Sim(a,b) I(a)nI(b)
• Bibliographic coupling the more common out-link
  neighbors, the more similar.
• Sim(a,b) O(a)nO(b)
• Extended Co-citation and Bibliographic Coupling
  (ECBC)
• ECBC the more common neighbors, the more
  similar.
• Sim(a,b) aI(a)nI(b) (1-a)O(a)nO(b),
  where 0a1 is a constant.

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3. Extending Link-based Similarity Measures

• SimRank
• two pages are similar if they are linked to by
  similar pages
• (1) Sim(u,u)1 (2) Sim(u,v)0 if I(u) I(v)
  0.
• Recursive definition
• C is a constant between 0 and 1.
• The iteration starts with Sim(u,u)1, Sim(u,v)0
  if u? v.

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3. Extending Link-based Similarity Measures

• Extended SimRank
• two pages are similar if they have similar
  neighbors
• (1) Sim(u,u)1 (2) Sim(u,v)0 if I(u) I(v)
  0.
• Recursive definition
• C is a constant between 0 and 1.
• The iteration starts with Sim(u,u)1, Sim(u,v)0
  if u? v.

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3. Extending Link-based Similarity Measures

• PageSim
• weighted multi-hop version of Co-citation
  algorithm.
• (a) multi-hop in-link information, and
• (b) importance of web pages.
• Can be represented by any global scoring system
• PageRank scores, or
• Authoritative scores of HITS.

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3. Extending Link-based Similarity Measures

• PageSim (phase 1 feature propagation)
• Initially, each web page contains an unique
  feature information, which is represented by its
  PageRank score.
• The feature information of a web page is
  propagated along out-link hyperlinks at decay
  rate d. The PR score of u propagated to v is
  defined by

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3. Extending Link-based Similarity Measures

• PageSim (phase 2 similarity computation)
• A web page v stores the feature information of
  its and others in its Feature Vector FV(v).
• The similarity between web page u and v is
  computed by Jaccard measure Jain et al 88
• Intuition the more common feature information
  two web pages contain, the more similar they are.

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3. Extending Link-based Similarity Measures

• Extended PageSim (EPS)
• Propagating feature information of web pages
  along in-link hyperlinks at decay rate 1- d.
• Computing the in-link PS scores.
• EPS(u,v) in-link PS(u,v) out-link PS(u,v).

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3. Extending Link-based Similarity Measures

• Properties
• CC Co-citation, BC Bibliographic Coupling,
• ECBC Extended Co-citation and Bibliographic
  Coupling,
• SR SimRank, ESR Extended SimRank, PS PageSim,
  EPS Extended PageSim.
• Summary
• The extended versions consider more structural
  information.
• ESR and EPS are bi-directional multi-hop.
• In ESR, two web pages are not similar unless
  there are intermediate pages between them, even
  if they link to other (see Figure 1(2)).

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3. Extending Link-based Similarity Measures

• Case study Sim(a,b)
• Summary
• The extended algorithms are more flexible.
• EPS is able to handle more cases.

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4. Experimental Results

• Datasets
• CSE Web (CW) dataset
• A set of web pages crawled from
  http//cse.cuhk.edu.hk.
• 22,000 pages, 180,000 hyperlinks.
• The average number of in-links and out-links are
  8.6 and 7.7.
• Google Scholar (GS) dataset
• A set of articles crawled from Google Scholar
  searching engine.
• Start crawling by submitting web mining
  keywords to GS, and then following the Cited by
  hyperlinks.
• 20,000 articles, 154,000 citations.

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4. Experimental Results

• Evaluation Methods
• Cosine TFIDF similarity (for CW dataset)
• A commonly used text-based similarity measure.
• Related Articles (for GS dataset)
• A list of related articles to a query article
  provided by GS.
• Can be used as ground truth.
• Parameter Settings

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4. Experimental Results

• CC, BC vs ECBC
• CW data (left) x-axis top N results y-axis
  average cosine TFIDF of all pages.
• GS data (right) x-axis top N results y-axis
  average precision of all pages.

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4. Experimental Results

• SimRank vs Extended SimRank
• CW data (left) x-axis top N results y-axis
  average cosine TFIDF of all pages.
• GS data (right) x-axis top N results y-axis
  average precision of all pages.

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4. Experimental Results

• PageSim vs Extended PageSim
• CW data (left) x-axis top N results y-axis
  average cosine TFIDF of all pages.
• GS data (right) x-axis top N results y-axis
  average precision of all pages.

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4. Experimental Results

• Overall Accuracy of Algorithms

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5. Conclusion and Future Work

• Conclusion
• Extended Neighborhood Structure model
• Bi-direction and multi-hop
• Extend existing link-based similarity measures
• Co-citation, Bibliographic coupling, SimRank,
  PageSim
• Experiments
• Future Work
• Extend link-based algorithms based on ENS model
• Prove the convergence of the Extended SimRank
• Integrating link-based with text-based

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Publications

• Z. Lin, M. R. Lyu, and I. King. PageSim A novel
  link-based measure of web page similarity. In WWW
  '06 Proceedings of the 15th international
  conference on World Wide Web. Pages 1019-1020,
  Edinburgh, Scotland, 2006.
• Z. Lin, I. King, and M. R. Lyu. PageSim A novel
  link-based similarity measure for the World Wide
  Web. In WI 06 Proceedings of the 5th
  International Conference on Web Intelligence. ACM
  Press. To appear, 2006.
• Z. Lin, M. R. Lyu, and I. King. Extending
  Link-based Algorithms for Similar Web Pages with
  Neighborhood Structure. Submitted to WWW07.