Transcript Web Search Slides based on those of C. Lee Giles, who credits R.

Web Search
Slides based on those of C. Lee Giles, who credits R. Mooney, S. White, W. Arms
C. Manning, P. Raghavan, H. Schutze
Search Engine Strategies
Subject hierarchies
• Yahoo! , dmoz -- use of human indexing
Web crawling + automatic indexing
• General -- Google, Ask, Exalead, Bing
Mixed models
• Graphs - KartOO; clusters – Clusty (now yippy)
New ones evolving
2
Components of Web Search
Service
Components
• Web crawler
• Indexing system
• Search system
Considerations
• Economics
• Scalability
• Legal issues
3
Query Engine
Index
Interface
Indexer
Users
Crawler
Web
A Typical Web Search Engine
4
Business models for advertisers
• When someone enters a query related to your
business or product, your page
– Comes up first in SERP (Search Engine Result Page)
– Comes up on the first SERP page
• Otherwise, you will not get the business
• Completely dependent on search engines ranking
algorithm for organic SEO (Search Engine
Optimization)
• Google changes ranking
– Will other search engines follow?
5
6
Generations of search engines
• 0th - Library catalog
– Based on human created metadata
• 1st - Altavista
– First large comprehensive database
– Word based index and ranking
• 2nd - Google
– High relevance
– Link (connectivity) based importance
7
Motivation for Link Analysis
• First approach to query matching
– use standard information retrieval methods, cosine, TFIDF, ...
• The web is a different environment than the IR
context of a set collection. A different approach is
needed:
– Huge and growing number of pages
• Try “classification methods”, Google estimates:
about 1,330,000 pages.
• How to choose only 30-40 pages and rank them
suitably to present to the user?
– Content similarity is easily spammed.
• A page owner can repeat some words and add
many related words to boost the rankings of his
pages and/or to make the pages relevant to a large
number of queries.
9
Early hyperlinks
• Web pages are connected through hyperlinks,
which carry important information.
– Some hyperlinks: organize information at the same site.
– Other hyperlinks: point to pages on other Web sites.
Such out-going hyperlinks often indicate an implicit
conveyance of authority to the pages being pointed to.
• Those pages that are pointed to by many other
pages are likely to contain authoritative
information.
10
Hyperlink algorithms
• During 1997-1998, two most influential hyperlink based
search algorithms PageRank and HITS were reported.
• Both algorithms are related to social networks. They
exploit the hyperlinks of the Web to rank pages according
to their levels of “prestige” or “authority”.
– HITS: Jon Kleinberg (Cornel University), at Ninth Annual ACMSIAM Symposium on Discrete Algorithms, January 1998
– PageRank: Sergey Brin and Larry Page, PhD students from
Stanford University, at Seventh International World Wide Web
Conference (WWW7) in April, 1998.
• PageRank powers the Google search engine.
•
Impact of “Stanford University” in web search
– Google: Sergey Brin and Larry Page (PhD candidates in CS)
– Yahoo!: Jerry Yang and David Filo (PhD candidates in EE)
– HP, Sun, Cisco, …
11
Other uses
• Apart from search ranking, hyperlinks are also
useful for finding Web communities.
– A Web community is a cluster of densely linked
pages representing a group of people with a special
interest.
• Beyond explicit hyperlinks on the Web, links in
other contexts are useful too, e.g.,
– for discovering communities of named entities (e.g.,
people and organizations) in free text documents,
and
– for analyzing social phenomena in emails..
The Web as a Directed Graph
Page A
Anchor
hyperlink
Page B
Assumption 1: A hyperlink between pages denotes
author perceived relevance (quality signal)
Assumption 2: The anchor of the hyperlink
describes the target page (textual context)
13
Anchor Text Indexing
• Extract anchor text (between <a> and </a>) of each
link followed.
• Anchor text is usually descriptive of the document to
which it points.
• Add anchor text to the content of the destination page
to provide additional relevant keyword indices.
• Used by Google:
– <a href=“http://www.microsoft.com”>Evil Empire</a>
– <a href=“http://www.ibm.com”>IBM</a>
Anchor text
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Anchor Text
WWW Worm - McBryan [Mcbr94]
• For ibm how to distinguish between:
– IBM’s home page (mostly graphical)
– IBM’s copyright page (high term freq. for ‘ibm’)
– Rival’s spam page (arbitrarily high term freq.)
“ibm”
A million pieces of
anchor text with “ibm”
send a strong signal
“ibm.com”
“IBM home page”
www.ibm.com
15
Indexing anchor text
• When indexing a document D, include
anchor text from links pointing to D.
Armonk, NY-based computer
giant IBM announced today
www.ibm.com
Joe’s computer hardware links
Compaq
HP
IBM
Big Blue today announced
record profits for the quarter
16
Indexing anchor text
• Can sometimes have unexpected side effects - e.g.,
french military victories
• Helps when descriptive text in destination page is
embedded in image logos rather than in accessible
text.
• Many times anchor text is not useful:
– “click here”
• Increases content more for popular pages with many
in-coming links, increasing recall of these pages.
• May even give higher weights to tokens from anchor
text.
17
Query length statistics
• See
http://www.keyworddiscovery.com/key
word-stats.html
• Statistics related to length of query on
the top search engines and the market
share of the search engines
20
Concept of Relevance
Document measures
Relevance, as conventionally defined, is binary (relevant
or not relevant). It is usually estimated by the similarity
between the terms in the query and each document.
Importance measures documents by their likelihood of
being useful to a variety of users. It is usually estimated
by some measure of popularity.
Web search engines rank documents by combination of
relevance and importance. The goal is to present the
user with the most important of the relevant documents.
21
Ranking Options
1. Paid advertisers
2. Manually created classification
3. Vector space ranking with corrections for document
length
4. Extra weighting for specific fields, e.g., title, anchors,
etc.
5. Popularity or importance, e.g., PageRank
Not all these factors are made public.
22
History of link analysis
• Bibliometrics
– Citation analysis since the 1960’s
– Citation links to and from documents
• Basis of pagerank idea
23
Bibliometrics
Techniques that use citation analysis to measure the
similarity of journal articles or their importance
Bibliographic coupling: two papers that cite many of the
same papers
Co-citation: two papers that were cited by many of the same
papers
Impact factor (of a journal): frequency with which the
average article in a journal has been cited in a particular year
or period
Citation frequency
24
Citation Graph
Bibliographic coupling
cites
Paper
is cited by
cocitation
Note that journal citations nearly always refer to earlier
work.
26
Graphical Analysis of Hyperlinks
on the Web
This page links to
many other pages
(hub)
1
2
4
3
5
Many pages
link to this
page
(authority)
6
27
Bibliometrics: Citation Analysis
• Many standard documents include
bibliographies (or references), explicit citations
to other previously published documents.
• Using citations as links, standard corpora can
be viewed as a graph.
• The structure of this graph, independent of
content, can provide interesting information
about the similarity of documents and the
structure of information.
• Impact of a paper!
29
Impact Factor
• Developed by Garfield in 1972 to measure the
importance (quality, influence) of scientific journals.
• Measure of how often papers in the journal are cited
by other scientists.
• Computed and published annually by the Institute for
Scientific Information (ISI).
• The impact factor of a journal J in year Y is the average
number of citations (from indexed documents
published in year Y) to a paper published in J in year
Y1 or Y2.
• Does not account for the quality of the citing article.
30
Citations vs. Links
• Web links are a bit different from
citations:
– Many links are navigational.
– Many pages with high in-degree are portals
not content providers.
– Not all links are endorsements.
– Company websites don’t point to their
competitors.
– Citations to relevant literature is enforced
by peer-review.
33
Ranking: query (in)dependence
• Query independent ranking
– Important pages; no need for queries
– Trusted pages?
– Pagerank can do this
• Query dependent ranking
– Combine importance with query evaluation
– Hits is query based.
34
Authorities
• Authorities are pages that are recognized
as providing significant, trustworthy, and
useful information on a topic.
• In-degree (number of pointers to a page)
is one simple measure of authority.
• However in-degree treats all links as
equal.
• Should links from pages that are
themselves authoritative count more?
35
Hubs
• Hubs are index pages that provide lots of
useful links to relevant content pages
(topic authorities).
• Ex: pages are included in the course
home page
36
Hyperlink-Induced Topic Search
(HITS)
• In response to a query, instead of an ordered list
of pages each meeting the query, find two sets of
inter-related pages:
– Hub pages are good lists of links on a subject.
• e.g., “Bob’s list of cancer-related links.”
– Authority pages occur recurrently on good hubs for the
subject.
• Best suited for “broad topic” queries rather than
for page-finding queries.
• Gets at a broader slice of common opinion.
37
HITS
• Algorithm developed by Kleinberg in 1998.
• IBM search engine project
• Attempts to computationally determine
hubs and authorities on a particular topic
through analysis of a relevant subgraph of
the web.
• Based on mutually recursive facts:
– Hubs point to lots of authorities.
– Authorities are pointed to by lots of hubs.
38
Hubs and Authorities
• Together they tend to form a bipartite
graph:
Hubs
Authorities
39
HITS Algorithm
• Computes hubs and authorities for a
particular topic specified by a normal
query.
– Thus query dependent ranking
• First determines a set of relevant pages
for the query called the base set S.
• Analyze the link structure of the web
subgraph defined by S to find authority
and hub pages in this set.
40
Constructing a Base Subgraph
• For a specific query Q, let the set of documents
returned by a standard search engine be called
the root set R.
• Initialize S to R.
• Add to S all pages pointed to by any page in R.
• Add to S all pages that point to any page in R.
S
R
41
Base Limitations
• To limit computational expense:
– Limit number of root pages to the top 200 pages
retrieved for the query.
– Limit number of “back-pointer” pages to a random
set of at most 50 pages returned by a “reverse link”
query.
• To eliminate purely navigational links:
– Eliminate links between two pages on the same
host.
• To eliminate “non-authority-conveying” links:
– Allow only m (m  48) pages from a given host as
pointers to any individual page.
42
Authorities and In-Degree
• Even within the base set S for a given
query, the nodes with highest in-degree
are not necessarily authorities (may just
be generally popular pages like Yahoo or
Amazon).
• True authority pages are pointed to by a
number of hubs (i.e. pages that point to
lots of authorities).
43
Iterative Algorithm
• Use an iterative algorithm to slowly converge
on a mutually reinforcing set of hubs and
authorities.
• Maintain for each page p  S:
– Authority score: ap (vector a)
– Hub score:
hp (vector h)
• Initialize all ap = hp = 1
• Maintain normalized scores:
 a   1  h p 
2
2
p
pS
p S
1
44
Convergence
• Algorithm converges to a fix-point if iterated indefinitely.
• Define A to be the adjacency matrix for the subgraph defined
by S.
– Aij = 1 for i  S, j  S iff ij
• Authority vector, a, converges to the principal eigenvector of
ATA
• Hub vector, h, converges to the principal eigenvector of AAT
• In practice, 20 iterations produces fairly stable results.
45
HITS Results
• An ambiguous query can result in the principal
eigenvector only covering one of the possible
meanings.
• Non-principal eigenvectors may contain hubs
& authorities for other meanings.
• Example: “jaguar”:
– Atari video game (principal eigenvector)
– NFL Football team (2nd non-princ. eigenvector)
– Automobile (3rd non-princ. eigenvector)
• Reportedly used by Ask.com
46
Google Background
“Our main goal is to improve the quality of
web search engines”
• Google  googol = 10^100
• Originally part of the Stanford digital
library project known as WebBase,
commercialized in 1999
47
Initial Design Goals
• Deliver results that have very high precision even
at the expense of recall
• Make search engine technology transparent, i.e.
advertising shouldn’t bias results
• Bring search engine technology into academic
realm in order to support novel research activities
on large web data sets
• Make system easy to use for most people, e.g.
users shouldn’t have to specify more than a few
words
48
Google Search Engine Features
Two main features to increase result precision:
• Uses link structure of web (PageRank)
• Uses text surrounding hyperlinks to improve
accurate document retrieval
Other features include:
• Takes into account word proximity in documents
• Uses font size, word position, etc. to weight word
• Storage of full raw html pages
49
PageRank in Words
Intuition:
• Imagine a web surfer doing a simple random walk
on the entire web for an infinite number of steps.
• Occasionally, the surfer will get bored and instead
of following a link pointing outward from the
current page will jump to another random page.
• At some point, the percentage of time spent at
each page will converge to a fixed value.
• This value is known as the PageRank of the page.
See Also: http://www.webworkshop.net/pagerank.html
50
PageRank
• Link-analysis method used by Google (Brin
& Page, 1998).
• Does not attempt to capture the distinction
between hubs and authorities.
• Ranks pages just by authority.
• Query independent
• Applied to the entire web rather than a
local neighborhood of pages surrounding
the results of a query.
51
Initial PageRank Idea
• Just measuring in-degree (citation count) doesn’t
account for the authority of the source of a link.
• Initial page rank equation for page p:
R( q )
R( p )  c 
q:q  p N q
– Nq is the total number of out-links from page q.
– A page, q, “gives” an equal fraction of its authority to all
the pages it points to (e.g. p).
– c is a normalizing constant set so that the rank of all
pages always sums to 1.
52
Initial PageRank Idea (cont.)
• Can view it as a process of PageRank
“flowing” from pages to the pages they
cite.
.1
.05
.08
.05
.03
.09
.03
.08
.03
.03
53
Initial Algorithm
• Iterate rank-flowing process until
convergence:
Let S be the total set of pages.
Initialize pS: R(p) = 1/|S|
Until ranks do not change (much) (convergence)
For each pS:
R( q )
R( p)  
q:q  p N q
c  1 /  R( p)
pS
For each pS: R(p) = cR´(p) (normalize)
54
Sample Stable Fixed Point
0.4
0.2
0.2
0.4
0.2
0.2
0.4
55
Problem with Initial Idea
• A group of pages that only point to
themselves but are pointed to by other
pages act as a “rank sink” and absorb all
the rank in the system.
Rank flows into
cycle and can’t get out
56
Rank Source
• Introduce a “rank source” E that
continually replenishes the rank of each
page, p, by a fixed amount E(p).


R
(
q
)
R ( p )  c 
 E ( p) 
 q:q  p N

q


57
PageRank Algorithm
Let S be the total set of pages.
Let pS: E(p) = /|S| (for some 0<<1, e.g. 0.15)
Initialize pS: R(p) = 1/|S|
Until ranks do not change (much) (convergence)
For each pS:
R( q )
R( p)  
 E ( p)
q:q  p N q
c  1 /  R( p)
pS
For each pS: R(p) = cR´(p) (normalize)
58
Random Surfer Model
• PageRank can be seen as modeling a “random surfer”
that starts on a random page and then at each point:
– With probability E(p) randomly jumps to page p.
– Otherwise, randomly follows a link on the current page.
• R(p) models the probability that this random surfer
will be on page p at any given time.
• “E jumps” are needed to prevent the random surfer
from getting “trapped” in web sinks with no outgoing
links.
59
Justifications for using PageRank
• Attempts to model user behavior
• Captures the notion that the more a
page is pointed to by “important” pages,
the more it is worth looking at
• Takes into account global structure of
web
60
Speed of Convergence
• Early experiments on Google used 322
million links.
• PageRank algorithm converged (within
small tolerance) in about 52 iterations.
• Number of iterations required for
convergence is empirically O(log n)
(where n is the number of links).
• Therefore calculation is quite efficient.
61
Google Ranking
• Complete Google ranking includes (based on
university publications prior to
commercialization).
–
–
–
–
Vector-space similarity component.
Keyword proximity component.
HTML-tag weight component (e.g. title preference).
PageRank component.
• Details of current commercial ranking functions
are trade secrets.
– Pagerank becomes Googlerank!
62
Personalized PageRank
• PageRank can be biased (personalized) by
changing E to a non-uniform distribution.
• Restrict “random jumps” to a set of
specified relevant pages.
• For example, let E(p) = 0 except for one’s
own home page, for which E(p) = 
• This results in a bias towards pages that are
closer in the web graph to your own
homepage.
63
Google PageRank-Biased Crawling
• Use PageRank to direct (focus) a crawler
on “important” pages.
• Compute page-rank using the current set
of crawled pages.
• Order the crawler’s search queue based
on current estimated PageRank.
64
Link Analysis Conclusions
• Link analysis uses information about the
structure of the web graph to aid search.
• It is one of the major innovations in web
search.
• It is the primary reason for Google’s
success.
• Still lots of research regarding
improvements
Limits of Link Analysis
• Stability
– Adding even a small number of nodes/edges to the
graph has a significant impact
• Topic drift
– A top authority may be a hub of pages on a
different topic resulting in increased rank of the
authority page
• Content evolution
– Adding/removing links/content can affect the
intuitive authority rank of a page requiring
recalculation of page ranks
From the Brin and Page
paper describing Google
Google Architecture (cont.)
Multiple crawlers run in parallel.
Each crawler keeps its own DNS
lookup cache and ~300 open
connections open at once.
Keeps track of URLs
that have and need
to be crawled
Compresses and
stores web pages
Stores each link and
text surrounding link.
Converts relative URLs
into absolute URLs.
Uncompresses and parses
documents. Stores link
information in anchors file.
Contains full html of every web
page. Each document is prefixed
by docID, length, and URL.
Google Architecture (cont.)
Maps absolute URLs into docIDs stored in Doc
Index. Stores anchor text in “barrels”.
Generates database of links (pairs of docIds).
Parses & distributes hit lists into
“barrels.”
Partially sorted forward
indexes sorted by docID. Each
barrel stores hitlists for a given
range of wordIDs.
In-memory hash table that
maps words to wordIds.
Contains pointer to doclist in
barrel which wordId falls into.
Creates inverted index
whereby document list
containing docID and hitlists
can be retrieved given wordID.
DocID keyed index where each entry includes info such as pointer to doc in
repository, checksum, statistics, status, etc. Also contains URL info if doc
has been crawled. If not just contains URL.
Google Architecture (cont.)
2 kinds of barrels. Short
barrell which contain hit
list which include title or
anchor hits. Long barrell
for all hit lists.
New lexicon keyed by
wordID, inverted doc
index keyed by docID,
and PageRanks used to
answer queries
List of wordIds produced
by Sorter and lexicon
created by Indexer used
to create new lexicon
used by searcher. Lexicon
stores ~14 million words.
Growth of Web Searching
In November 1997:
• AltaVista was handling 20 million searches/day.
• Google forecast for 2000 was 100s of millions of
searches/day.
In 2004, Google reports 250 million webs searches/day, and
estimates that the total number over all engines is 500 million
searches/day.
Moore's Law and web searching
In 7 years, Moore's Law predicts computer power will increase
by a factor of at least 24 = 16.
It appears that computing power is growing at least as fast as
web searching.
Growth of Google
In 2000: 85 people
50% technical, 14 Ph.D. in Computer Science
In 2000: Equipment
2,500 Linux machines
80 terabytes of spinning disks
30 new machines installed daily
By fall 2002, Google had grown to over 400 people.
In 2004, Google hired 1,000 new people.
As of 2008, 16,800 employees, $15 billion in sales => $1 million average
earnings/employee
Google Status July 2006
• Nearly 500,000 linux boxes (servers)
• 20 billion pages and counting
• 100 million queries a day
Google Status - August 2007
Google Status - October 2008
Google Status - October 2009
Google Status - July 2010
What’s coming?
• More personal search
• Social search
• Mobile search
• Specialty search
• Freshness search
3rd generation search?
Will anyone replace Google?
“Search as a problem is only 5% solved” Udi Manber,
1st Yahoo, 2nd Amazon, now Google
Google of the future