1 Extending Link-based Algorithms for Similar Web Pages with Neighborhood Structure Allen, Zhenjiang LIN CSE, CUHK 13 Dec 2006

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

3 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.

4 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

5 1. Introduction Searching the Web  Keyword searching  Similarity searching Search Engine KEYWORDS: news … Search Engine URL: … similarity measure

6 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 1. Introduction Focus of this talk

7 Extended Neighborhood Structure (ENS) model  Question: what hide in hyperlinks? similarity relationship between pages, similarity relationship decrease along hyperlinks. 2. Extend Neighborhood Structure Model

8 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.

9 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(a 1, a s ): a sequence of vertices a 1, a 2, …, a s such that (a i, a i+1 ) ∈ E (i=1,…,s-1) and a i 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.

10 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) ∩ I(b)|  Bibliographic coupling: the more common out-link neighbors, the more similar. Sim(a,b) = |O(a) ∩ O(b)| Extended Co-citation and Bibliographic Coupling (ECBC)  ECBC: the more common neighbors, the more similar. Sim(a,b) = α|I(a) ∩ I(b)| + (1 - α)|O(a) ∩ O(b)|, where 0≤α≤1 is a constant.

11 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.

12 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.

13 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.

14 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

15 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.

16 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).

17 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)). 3. Extending Link-based Similarity Measures

18 Case study: Sim(a,b)  Summary The extended algorithms are more flexible. EPS is able to handle more cases. 3. Extending Link-based Similarity Measures

19 4. Experimental Results Datasets  CSE Web (CW) dataset: A set of web pages crawled from 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.

20 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

21 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.

22 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.

23 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.

24 4. Experimental Results Overall Accuracy of Algorithms

25 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

26 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 , Edinburgh, Scotland, 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, Z. Lin, M. R. Lyu, and I. King. Extending Link-based Algorithms for Similar Web Pages with Neighborhood Structure. Submitted to WWW’07.