Thanks to R. Mooney, S. White, W. ArmsC. Manning, P. Raghavan, H. Schutze

Search Engines

How Google Works:

Connectivity and

Link Analysis

What we have covered

• What is IR• Evaluation• Tokenization and properties of text • Web crawling• Query models• Vector methods• Measures of similarity• Indexing• Inverted files• Web characteristics

– Graph, links– Spam, SEO, duplication

• Google and Link Analysis

What we will cover• Search engines

– Classifications– Business models

• Ranking by importance– Link analysis (graph search)

• Citations• PageRank• Hubs/authorities

• Google– Role in search– Architecture

Google search

Web Search

Goal

Provide information discovery for large amounts of open access material on the web

Challenges

• Volume of material -- several billion items, growing steadily

• Items created dynamically or in databases

• Great variety -- length, formats, quality control, purpose, etc.

• Inexperience of users -- range of needs

• Economic models to pay for the service

Search Engine StrategiesTypes of search engines

Broad Classes:

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)

Metasearch

New ones evolving

Many Aspects of a Web Search Service

Computational Components

• Web crawler

• Indexing system

• Search system

Social Considerations

• Economics

• Scalability

• Legal issues

Interface

Query Engine

Indexer

Index

Crawler

Users

Web

A Typical Web Search Engine

Ranking by web graph

Business Models

Subscription

Monthly fee with logon provides unlimited access (introduced by InfoSeek)

Advertising

Access is free, with display advertisements (introduced by Lycos)

Can lead to distortion of results to suit advertisers

Focused advertising - Google, Overture

Licensing

Cost of company are covered by fees, licensing of software and specialized services

Others?

Business models for advertisers

• For a related query “tennis shoes”• Your company

– Comes up first in SERP– Comes up on the first SERP page

• Otherwise, say goodbye to web business• Completely dependent on search engines ranking

algorithm for organic SEO• Google changes ranking

– Will other search engines follow?

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

Motivation for Link Analysis

• Early search engines mainly compare content similarity of the query and the indexed pages. I.e., – They use information retrieval methods, cosine, TF-IDF, ...

• From mid 90’s, it became clear that content similarity alone was no longer sufficient. – The number of pages grew rapidly in the mid-late 1990’s.

• Try “classification methods”, Google estimates: millions of relevant 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.

Early hyperlinks

• Starting mid 90’s, researchers began to work on the problem, resorting to hyperlinks. – In Feb, 1997, Yanhong Li (Scotch Plains, NJ) filed a hyperlink

based search patent. The method uses words in anchor text of hyperlinks.

• Web pages on the other hand are connected through hyperlinks, which carry important information. – Manually made – social network– Some hyperlinks: organize information at the same site. – Other hyperlinks: point to pages from 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.

Hyperlink algorithms

• During 1997-1998, the 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 ACM-SIAM

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, …

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

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)

Page Ahyperlink Page BAnchor

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

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.)

www.ibm.com

“ibm” “ibm.com” “IBM home page”

A million pieces of anchor text with “ibm” send a strong signal

Indexing anchor text

• When indexing a document D, include anchor text from links pointing to D.

www.ibm.com

Armonk, NY-based computergiant IBM announced today

Joe’s computer hardware linksCompaqHPIBM

Big Blue today announcedrecord profits for the quarter

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.

Anchor Text

• Other applications– Weighting/filtering links in the graph

• HITS [Chak98], Hilltop [Bhar01]

– Generating page descriptions from anchor text [Amit98, Amit00]

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.

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

Many of these factors are NOT made public.

History of link analysis

• Bibliometrics– Citation analysis since the 1960’s– Citation links to and from documents

• Basis of pagerank idea

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

Cocitation: 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

Citation Graph

Paper

cites

is cited by

Note that academic citations nearly always refer to earlier work.

Bibliographic coupling

cocitation

Graphical Analysis of Hyperlinks on the Web

This page links to many other pages (hub)

Many pages link to this page (authority)

1

2

34

5 6

Search Engines

• What is connectivity/linking?• Role of connectivity in ranking

– Academic paper analysis– Hits - IBM– Ask– Facebook– Google– Google Scholar– CiteSeer (CiteSeerX), ChemXSeer, ArchSeer, etc– …

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 paper!

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 Y1 or Y2.

• Does not account for the quality of the citing article.

Citations vs. Web Links

• Web links are a bit different than 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.

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.– So is GoogleRank (combine PageRank with

some similarity measure)

PageRank vs Hits

• Web site classification– Hubs– Authorities

• Hubs & Authorities: Hits

• Authorities: PageRank

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?

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

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.

Hubs and Authorities

• Together they tend to form a bipartite graph:

Hubs Authorities

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.

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.

R

S

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 48) pages from a given host as

pointers to any individual page.

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).

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:

€

hp( )2

p∈S

∑ =1

€

ap( )2

p∈S

∑ =1

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 ij

• 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.

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 first used by Teoma.com

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.

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

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 couple words

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

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.

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.

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:

– 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.

∑→

=pqq qN

qRcpR

:

)()(

Initial PageRank Idea (cont.)

• Can view it as a process of PageRank “flowing” from pages to the pages they cite.

.1

.09

.05

.05

.03

.03

.03

.08

.08

.03

Initial Algorithm

• Iterate rank-flowing process until convergence: Let S be the total set of pages.

Initialize pS: R(p) = 1/|S|

Until ranks do not change (much) (convergence)

For each pS:

For each pS: R(p) = cR´(p) (normalize)

∑→

=′pqq qN

qRpR

:

)()(

∑∈

′=Sp

pRc )(/1

Sample Stable Fixed Point

0.4

0.4

0.2

0.2

0.2

0.2

0.4

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 intocycle and can’t get out

Rank Source

• Introduce a “rank source” E that continually replenishes the rank of each page, p, by a fixed amount E(p).

⎟⎟⎠

⎞⎜⎜⎝

⎛+= ∑

→

)()(

)(:

pEN

qRcpR

pqq q

PageRank Algorithm

Let S be the total set of pages.

Let pS: E(p) = /|S| (for some 0<<1, e.g. 0.15)

Initialize pS: R(p) = 1/|S|

Until ranks do not change (much) (convergence)

For each pS:

For each pS: R(p) = cR´(p) (normalize)

)()(

)(:

pEN

qRpR

pqq q

+=′ ∑→

∑∈

′=Sp

pRc )(/1

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.

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

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.

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!

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.

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.

Information Retrieval Using PageRank

•Remember no queries are used with PageRank

•PageRank precomputed (static rank)

•Query independent ranking

How are queries put into the final ranking?

Simple Method

Combine term similarity (dynamic rank) with PageRank

One method:

Final rank = R(p) * Similarity

Lucene index: add to boasting term

Combining Term Weighting with PageRank – another approach

Combined Method

1. Find all documents that share a term with the query vector.

2. The similarity, using conventional term weighting, between the query and document j is sj.

3. The rank of document j using PageRank or other graph ranking is pj.

4. Calculate a combined rank cj = sj + (1- )pj, where is a constant.

5. Display the hits ranked by cj.

This method is used in several commercial systems, but the details have not been published.

Why we need a fast link-based rank?

“…The link structure of the Web is significantly more dynamic than the contents on the Web. Every week, about 25% new links are created. After a year, about 80% of the links on the Web are replaced with new ones. This result indicates that search engines need to update link-based ranking metrics very often…”

[ Cho et al., 04 ]

Accelerating PageRank

• Web Graph Compression to fit in internal memory [Boldi et al., 04]

• Efficient External memory implementation [Haveliwala, 99; Chen et al., 02]

• Mathematical approaches

• Combination of the above strategies

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

Google Architecture - preliminary

Implemented in Perl, C and C++ on Solaris and LinuxImplemented in Perl, C and C++ on Solaris and Linux

Preliminary

“Hitlist” is defined as list of occurrences of a particular word in a particular document including additional meta info:- position of word in doc

- font size - capitalization - descriptor type, e.g. title, anchor, etc.

Google Architecture (cont.)

Keeps track of Keeps track of URLs that have URLs that have and need to be and need to be

crawledcrawled

Compresses and Compresses and stores web pagesstores web pages

Multiple crawlers run in Multiple crawlers run in parallel. Each crawler keeps parallel. Each crawler keeps its own DNS lookup cache its own DNS lookup cache

and ~300 open connections and ~300 open connections open at once.open at once.

Uncompresses and Uncompresses and parses documents. parses documents.

Stores link information Stores link information in anchors file.in anchors file.

Stores each link Stores each link and text and text

surrounding link.surrounding link.

Converts relative Converts relative URLs into absolute URLs into absolute

URLs.URLs.

Contains full html of every Contains full html of every web page. Each document is web page. Each document is

prefixed by docID, length, prefixed by docID, length, and URL.and URL.

Google Architecture (cont.)Maps absolute URLs into docIDs stored in Maps absolute URLs into docIDs stored in

Doc Index. Stores anchor text in Doc Index. Stores anchor text in “barrels”. Generates database of links “barrels”. Generates database of links

(pairs of docIds).(pairs of docIds).

Parses & distributes hit lists Parses & distributes hit lists into “barrels.”into “barrels.”

Creates inverted index Creates inverted index whereby document list whereby document list containing docID and containing docID and

hitlists can be retrieved hitlists can be retrieved given wordID.given wordID.

In-memory hash table that In-memory hash table that maps words to wordIds. maps words to wordIds.

Contains pointer to doclist Contains pointer to doclist in barrel which wordId falls in barrel which wordId falls

into. into.

Partially sorted forward Partially sorted forward indexes sorted by docID. indexes sorted by docID. Each barrel stores hitlists Each barrel stores hitlists

for a given range of for a given range of wordIDs.wordIDs.

DocID keyed index where each entry includes info such as pointer DocID keyed index where each entry includes info such as pointer to doc in repository, checksum, statistics, status, etc. Also to doc in repository, checksum, statistics, status, etc. Also

contains URL info if doc has been crawled. If not just contains URL.contains URL info if doc has been crawled. If not just contains URL.

Google Architecture (cont.)

List of wordIds List of wordIds produced by Sorter produced by Sorter

and lexicon created by and lexicon created by Indexer used to create Indexer used to create new lexicon used by new lexicon used by

searcher. Lexicon searcher. Lexicon stores ~14 million stores ~14 million

words.words.

New lexicon keyed by New lexicon keyed by wordID, inverted doc wordID, inverted doc

index keyed by docID, index keyed by docID, and PageRanks used and PageRanks used

to answer queriesto answer queries

2 kinds of barrels. 2 kinds of barrels. Short barrell which Short barrell which

contain hit list which contain hit list which include title or anchor include title or anchor hits. Long barrell for hits. Long barrell for

all hit lists.all hit lists.

Scalability

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

1,000,000,000

10,000,000,000

1994 1997 2000

The growth of the web

Web search services are distributed centralized systems

• Over the past 9 years, Moore's Law has enabled the services to keep pace with the growth of the web and the number of users, while adding extra function.

• Will this continue?

• Possible areas for concern are: staff costs, telecommunications costs, disk access rates.

Scalability

Scalability: Performance

Very large numbers of commodity computersAlgorithms and data structures scale linearly

• Storage– Scale with the size of the Web– Compression/decompression

• System– Crawling, indexing, sorting simultaneously

• Searching– Bounded by disk I/O

Growth of GoogleIn 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

A 2005 estimate by Paul Strassmann has 200,000 servers, while unspecified sources claimed this number to be upwards of 450,000 in 2006.2011, 900,000 servers

By fall 2002, Google had grown to over 400 people.

In 2004, Google hired 1,000 new people.

2008, 16,800 employees, $15 billion in sales => $1 million average earnings/employee2011, 32,500 employees, $40 billion in sales2012, 53,900 employees, $50 billion in sales

PlatformCustom SoftwareGoogle Web Server (GWS) — Custom Linux-based Web server that Google uses for its

online services.Storage systems:

Google File System and its successor, ColossusBigTable — structured storage built upon GFS/ColossusSpanner — planet-scale structured storage system, next generation of BigTable stackGoogle F1 — a distributed, quasi-SQL DBMS based on Spanner, substituting a custom version of MySQL.

Chubby lock serviceBorg — job scheduling and monitoring systemMapReduce and Sawzall programming languageIndexing/search systems:

TeraGoogle — Google's large search index (launched in early 2006), designed by Anna Paterson of Cuil fame.

Caffeine (Percolator) — continuous indexing system (launched in 2010).

Private Networks3rd largest ISPData CentersThroughout the world

Scalability: Staff

Programming: Have very well trained staff. Isolate complex code.

System maintenance: Organize for minimal staff (e.g., automated log analysis, do not fix broken computers).

Customer service: Automate everything possible, but complaints, large collections, etc. require staff.

Evaluation Web Searching

Test corpus must be dynamic

The web is dynamic (10%-20%) of URLs change every month

Spam methods change change continually

Queries are time sensitive

Topic are hot and then not

Need to have a sample of real queriesNew emphasis, 2007, QDF: “query deserves freshness”

Languages

At least 90 different languages

Reflected in cultural and technical differences

Amil Singhal, Google, 2004

Other Uses of Web Crawling and Associated Technology

The technology developed for web search services has many other applications.

Conversely, technology developed for other Internet applications can be applied in web searching

• Related objects (e.g., Amazon's "Other people bought the following").

• Recommender and reputation systems (e.g., ePinion's reputation system).

ComScore global share

Number of search engine queries - US

About half a billion per day

Dec 2012 2 billion internet users

2012

Second generation Web search

• Ranking using web specific data– HTML tag information– click stream information (DirectHit)

• people vote with their clicks

– directory information (Yahoo! directory)– anchor text– link analysis

Link Analysis Ranking - Google

• Intuition: a link from q to p denotes endorsement – people vote with their links

• Popularity count– rank according to the incoming links

• PageRank algorithm– perform a random walk on the Web graph. The pages visited most

often are the ones most important.

( )n1

á1F(q)

PR(q)áPR(p)

pq

−+= ∑→

Second generation SE performance

• Good performance for answering navigational queries– “finding needle in a haystack”

• … and informational queries– e.g “oscar winners”

• Resistant to text spamming

• Generated substantial amount of research

• Latest trend: specialized search engines

Result evaluation

• recall becomes useless

• precision measured over top-10/20 results

• Shift of interest from “relevance” to “authoritativeness/reputation” or importance

• ranking becomes critical

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

Are Google rankings fair?Are any search engines rankings fair?

• Try out certain queries– “Jew” USA– “Jew” Germany– “tiananmen square”

• Google.com

• Google.cn

• Baidu.com

What is role of search engines in society

• Should these queries be banned?– “how do I make a dirty bomb”– “how do I make a black hole”

• Do search engines have a social responsibility?

Social implications of public search engines

• What is the web role of search in society– Human flesh search engine

– What is the responsibility of search engines?

• The googlization of everything• Should governments control search engines

– Positions• Search is too important to be left to the private sector

• Search will only work from 8 to 5 on weekdays and will be off on holidays.

– New Chinese government search engine• Panguso

Google vs Bing

Google of the future

What next? GoogleFace or Faceoogle

What we covered• Search engines types and business models• Ranking by importance

– Link analysis• Citations • Pagerank (query independent; must be combined with a lexical

similarity)• Hubs/authorities (query dependent)

• Google– Role in search– Architecture

• Covered the basics of modern search engines• Search engines are constantly evolving