Google Reasonable Surfer Model

1997

The Anatomy of a Large-Scale Hypertextual Web Search Engine

by Sergei Brin&Larry Page

2004

Ranking documents based on user behavior and/or feature data

Invented by Jeffrey A. Dean, Corin Anderson and Alexis Battle

"Systems and methods consistent with the principles of the invention may provide a reasonable surfer model that indicates that when a surfer accesses a document with a set of links, the surfer will follow some of the links with higher probability than others.

This reasonable surfer model reflects the fact that not all of the links associated with a document are equally likely to be followed. Examples of unlikely followed links may include “Terms of Service” links, banner advertisements, and links unrelated to the document."

A system generates a model based on feature data relating to different features of a link from a linking document to a linked document and user behavior data relating to navigational actions associated with the link. The system also assigns a rank to a document based on the model.

Link Features

1. Font size of anchor text associated with the link;

2. The position of the link (measured, for example, in a HTML list, in running text, above or below the first screenful viewed on an 800 X 600 browser display, side (top, bottom, left, right) of document, in a footer, in a sidebar, etc.);

3. If the link is in a list, the position of the link in the list;

4. Font color and/or other attributes of the link (e.g., italics, gray, same color as background, etc.);

5. Number of words in anchor text of a link;

6. Actual words in the anchor text of a link;

7. How commercial the anchor text associated with a link might be;

8. Type of link (e.g., text link, image link);

9. If the link is an image link, what the aspect ratio of the image might be;

10. The context of a few words before and/or after the link;

11. A topical cluster with which the anchor text of the link is associated;

12. Whether the link leads somewhere on the same host or domain;

13. If the link leads to somewhere on the same domain,

* whether the link URL is shorter than the referring URL; and/or

* whether the link URL embeds another URL (e.g., for server-side redirection)

1. The URL of the source document (or a portion of the URL of the source document);

2. A web site associated with the source document;

3. A number of links in the source document;

4. The presence of other words in the source document;

5. The presence of other words in a heading of the source document;

6. A topical cluster with which the source document is associated; and/or

7. A degree to which a topical cluster associated with the source document matches a topical cluster associated with anchor text of a link.

1. The URL of the target document (or a portion of the URL of the target document);

2. A web site associated with the target document;

3. Whether the URL of the target document is on the same host as the URL of the source document;

4. Whether the URL of the target document is associated with the same domain as the URL of the source document;

5. Words in the URL of the target document; and/or

6. The length of the URL of the target document.

User behavior data associated with documents and links may also be considered, such as:

1. Information about how people access and interact with documents, such as navigational actions (e.g., links selected, web addresses entered, forms completed, etc.),

2. The language of the users,

3. Interests of the users,

4. Query terms entered,

5. How often a link is selected,

6. How often links aren’t selected when one link is chosen,

7. How often no links are selected on a page,

8. etc.

2010

The rank of a document may be interpreted as the probability that a reasonable surfer will access the document after following a number of forward links.

These probabilities are used to determine a dynamic weight for each of the links that can influence how highly the pages they point to might rank. The different weights for the links might determine how much PageRank that each link passes along to other pages.

Cine sunt?

Nume: Dan Brasoveanu

Optimisation specialist

E-mail: dan.brasoveanu@nb.ro

Twitter.com/danny_re

Projects

Reasonable Surfer Model

Example:

Abstract:

Multumesc de timpul acordat.

Intrebari? :)

"PageRank can be thought of as a model of user behavior. We assume there is a “random surfer” who is given a web page at random and keeps clicking on links, never hitting “back” but eventually gets bored and starts on another random page. The probability that the random surfer visits a page is its PageRank."

PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))

Surse:

www.seobythesea.com

http://infolab.stanford.edu/~backrub/google.html

http://patft.uspto.gov/