1. Methods and Apparatus for Ranking Web Page Search Results
Alireza Abbasi
Industrial Information Technology
Patent Presentation
Information Technology Policy Program
Seoul National University

2. Bibliographic Details
Title:
Methods and Apparatus for Ranking Web Page Search Results
Inventor:
Andrei Z. Broader, Menlo Park, CA
Assignee:
Alta Vista Company, Palo Alto, CA (US)
Filed Date:
Oct
Bibliographic Details

3. Context Abstract Background of Invention Summery of Invention
Field of invention
Related Art
Summery of Invention
Advantages & Applications
Detailed Description
Context

4. Abstract To Rank the quality of page
Forming a linear combination of two/more matrices
Using the coefficients of the eigenvector of the resulting matrix
Abstract

5. Computerized Information Retrieval
Invention Relates generally to
Computerized Information Retrieval
Identifying related pages in a hyperlinked database environment (Web)
Challenge is to retrieve only the most relevant resources to the query
Field of Invention

6. Related Art (1) Kleinberg Algorithm:
Identify ‘Hub’ and ‘Authority’ pages in a neighborhood graph for a query
Determines related pages starting with a single page.
Problems:
Doesn’t deal with popular URLs
Doesn’t analyze contents of pages
when it is computing the most related pages
Related Art (1)

7. Related Art (2) Google Search Engine:
Use ‘PageRank’ to prioritize the results.
PageRank
Page A has pages T1…Tn which point to it
d: [0…1] damping factor
C(A): # of pages going out of page A
PR(A)=(1-d) + d( PR(T1)/C(T1)+…+PR(Tn)/C(Tn) )
Sum of all pages’ PageRank = 1
Problems:
Ranking is independent of the search query
No provision for externally evaluating sites
Related Art (2)

8. R. Lempel, S. Moran ‘SALSA’ (Stochastic Approach for Link-Structure Analysis)
Replace Kleinberg’s Mutual Reinforcement by stochastic method
the coupling between hubs & authorities is less tight
Based upon Markov chains
Related Art (3)

9. Summery of Invention (1)
Invention provides
“a method whereby a linear combination of matrices (pages’ information) can be used to rank the pages”
Highly relevant results to the user’ search
The coefficients of the eigenvector provide a measure of the quality of each page in related to the other pages.
Determining ranking categories based on # of pages to be ranked
Classifying each page in one of the categories
Summery of Invention (1)

10. Summery of Invention (2)
A fixed amount of storage for representing the rank of each page
Each bit represents one of the categories
Bit assigned to the page: The rank of each page
The eigenvector coefficients of neighboring pages can be used to generate a hub score
=> Small amount of storage and computational resources
Summery of Invention (2)

11. Fig 1. block diagram of a hyperlinked environment
Detailed Description
Improve Ranking Method
Fig 1. block diagram of a hyperlinked environment

12. A flow diagram of a method for ranking pages

13. Determine Matrices to Include in Leaner Combination
202
An example of matrices that maybe used in a method

14. Building Neighborhood Graph
Assume that:
Related pages will tend to be ‘near’ the selected page
The same keywords appear as part of the content of related pages.
an initial page is selected, page linked to that are represented as a graph in a memory
Patent ‘Method for identification related page in a hyperlinked database’
Building Neighborhood Graph

15. Building Adjacency Matrix
- a collection ‘C’ of web-sites a given topic ‘t’
- a root set ‘S’ of sites - Search engine query ‘q’
From S a base C which consist of Sites
in the root set S
which point a site in S
By using a search engine that stores linkage information
Patent ‘Web Page Connectivity Server’
Which are pointed by a set in S
C and its link structure directed Graph G
Directed edge ‘ij’ appears in G, if site ‘i‘ consist of a link to site ‘j’
|C|*|C| is adjacency matrix of G
Building Adjacency Matrix

16. Determining Attractor Matrices
Indication that is provided by viewer
Computerize utility program
Analyzes content and recognizes
Keywords, key phrases, page links, …
Possible to be runtime or offline and update periodically
Co-citation(GTG)
# of sites jointly cite the page index by i & j
Bibliographic coupling (GGT)
# of sites jointly referred to by the page index by i & j
Matrices can be included in linear combination
Determining Attractor Matrices

17. A flow diagram of a method for ranking pages according eigenvector coefficients

18. Ranking according to eigenvector coefficient
Minimal storage space
Neighborhood graph may be so large
Power low distribution
# sites whose eigenvector coefficient have a value that is less than a chosen number
Generating a hub score for one or more pages
Based on the sum (or a function of sum) of the eigenvector coefficient of neighboring pages
indication regarding the quality of pages as a hub or directory of other pages
provide information that is valuable for the user.
Ranking according to eigenvector coefficient

19. Ranking according to eigenvector coefficient
Example:
result of a query 0.5 billion pages (distributed geometrically)
1st category, high ranked page (50 pages)
2nd category, next high rank paged (geometric multiple of 50 pages) …
Each page is assigned to a category by designating a corresponding bit from a multi bit word
If 10 bits per page are allotted, 1024 categories are available
Ranking according to eigenvector coefficient

20. Advantage & Applications
The present invention is capable of being distributed as a program product in a variety of forms
Each block diagram component, flowchart step, etc can be implemented by a wide range of hardware, software, firmware or ….
Advantage & Applications

21. Thank you & Best wishes