Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 7.1 Chapter 7 : Navigating the Web Frustration

Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005

Slide 7.1

Chapter 7 : Navigating the Web

• Frustration in browsing and navigating.• Basic navigation tools.• Breadcrumb navigation.• Revisitation of web pages.• Hypertext orientation tools.• Starting points for navigation.• Web data mining.• Mining user navigation patterns.• The Best Trail algorithm.• Visualisation that aids navigation.


Slide 7.2

Frustration in Web Browsing and Navigation

• Frustrating experiences due to navigation are:– Lost connections.– Long download time of web pages.– Web pages that are not found (404 error).– Popup adverts.

• Browsing frustrations:– Badly designed web pages.– Unpredictable user interfaces.


Slide 7.3

Basic Navigation Tools

• Link marker – changes colour when clicked.

• Back button – stack-based, high use and recurrence rate.

• Bookmarks – insertion rate much higher than deletion rate.

• History lists – linear display, can search.

• Search engine toolbar.


Slide 7.4

Breadcrumb Navigation

Figure 7.3 : Navigation bar


Slide 7.5

What do web users do?

edrURLsVisitTotalNumbe

isitedumberURLsVDifferentNedrURLsVisitTotalNumbe

• Formula for recurrence rate – well above 50%

• There is about 40% chance that the next page visited is within 6 pages visited.

• Almost all users have 1-2 pages they revisit more often than others, e.g. their home page.


Slide 7.6

Hypertext Orientation Tools

• Figure 7.4 : Nielsen’s hypertext implemented in Apple’s Hypercard environment


Slide 7.7

Wired News 14/02/03, Marc Andreessen, one of the founders of Netscape, said

“If I had to do it over again, I'd probably show some sort of graphical representation of a tree, so you could see what path you're travelling on and could backtrack. I'd also include thumbnail renderings on the tree to show where you'd been.”


Slide 7.8

What is a good starting point?

• PageRank measures quality by recommendation, it does not measure whether a page is a “good” starting point for navigation.

• A starting page should be:– Relevant to the user’s goals.– Central, i.e. distance to other pages minimal.– Should be able to reach a maximum of other

pages, i.e. should be connected.


Slide 7.9

Potential Gain Computation

Iterate the following equations n times:

count = G * count

PG = PG + (f(d) * count)

G – adjacency matrix of the web graph.

count – vector of no. of tips from start.

PG – potential gain vector.

f(d) – discount fn, decreases with d.


Slide 7.10

Example Web Site


Slide 7.11 Web Page Potential Gain

Mark 0.2000

PhD 0.1354

WebTech 0.1209

Staff 0.1025

Azy 0.1025

Research 0.0958

Kevin 0.0748

SCSIS 0.0741

Students 0.0663

KLab 0.0277

WebDyn 0.0001


Slide 7.12

Web Data Mining

• Content mining – concerned with the information contained in web pages, e.g. text mining.

• Structure mining – concerned with link analysis.

• Usage mining – attempts to discover patterns in log data.


Slide 7.13 W3C Extended Log File FormatField Date Description

Date date The date that the activity occurredTime time The time that the activity occurredClient IP address c-ip The IP address of the client that accessed your server

User Name cs-usernameThe name of the autheticated user who access your server, anonymous users are represented by -

Servis Name s-sitename The Internet service and instance number that was accessed by a clientServer Name s-computername The name of the server on which the log entry was generatedServer IP Address s-ip The IP address of the server that accessed your serverServer Port s-port The port number the client is connected toMethod cs-method The action the client was trying to performURI Stem cs-uri-stem The resource accessedURI Query cs-uri-query The query, if any, the client was trying to performProtocol Status sc-status The status of the action, in HTTP or FTP termsWin32 Status sc-win32-status The status of the action, in terms used by Microsoft WindowsBytes Sent sc-bytes The number of bytes sent by the serverBytes Received cs-bytes The number of bytes received by the serverTime Taken time-taken The duration of time, in milliseconds, that the action consumedProtocol Version cs-version The protocol (HTTP, FTP) version used by the clientHost cs-host Display the content of the host header

User Agent cs(User Agent) The browser used on the clientCookie cs(Cookie) The content of the cookie sent or received, if any

Referrer cs(Referrer)The previous site visited by the user. This site provided a link to the current site

cs = client-to-server actions

s = server actionsc = client actions

sc = server-to-client actions


Slide 7.14

Analog – Web Log File Analyser

• Gives basic statistics such as– number of hits.– average hits per time period. – what are the popular pages in your site.– who is visiting your site. – what keywords are users searching for to get to you.– what is being downloaded.

• Log data does not disclose the visitor’s identity• What do analog’s reports mean?• Report for www.dcs.bbk.ac.uk/~mark


Slide 7.15

Applications of Usage Mining

• Pre-fetching and caching web pages

• E-commerce and clickstream analysis

• Web site reorganisation

• Personalisation

• Recommendation of links and products


Slide 7.16

Identification of User

• By IP address – Not so reliable as IP can be dynamic– Different users may use same IP

• Through cookies– Reliable but user may remove cookies– Security and privacy issues

• Through login– Users have to register


Slide 7.17

Sessionising

• Time oriented (robust)– By total duration of session

• not more than 30 minutes

– By page stay times (good for short sessions)• not more than 10 minutes per page

• Navigation oriented (good for short sessions and when timestamps unreliable)– Referrer is previous page in session, or– Referrer is undefined but request within 10 secs, or – Link from previous to current page in web site


Slide 7.18

Mining Navigation Patterns

• Each session induces a user trail through the site

• A trail is a sequence of web pages followed by a user during a session, ordered by time of access.

• A pattern in this context is a frequent trail.

• Co-occurrence of web pages is important, e.g. shopping-basket and checkout.

• Use a Markov chain model.


Slide 7.19

Trails inferred from Log data(Each session results in a trail)

ID Trail

1 A1 > A2 > A3

2 A1 > A2 > A3

3 A1 > A2 > A3 > A4

4 A5 > A2 > A4

5 A5 > A2 > A4 > A6

6 A5 > A2 > A3 > A6


Slide 7.20

The Markov Chain from the Data


Slide 7.21

Support and Confidence

• Support s in [0,1) – accept only trails whose initial probability is above s.– Setting support to be above the average click-

through is reasonable.

• Confidence c in [0,1) – accept only trails whose probability is above c. – The probability of a trail is obtained by

multiplying the transition probabilities of the links in the trail.


Slide 7.22

Mining Frequent Trails

• Find all trails whose initial probability is higher than s, and whose trail probability is above c.

• Use depth-first search on the Markov chain to compute the trails.

• The average time needed to find the frequent trails is proportional to the number of web pages in the site.


Slide 7.23

Frequent Trails Support = 0.1 and Confidence = 0.3

Trail Probability

A1 > A2 > A3 0.67

A5 > A2 > A3 0.67

A2 > A3 0.67

A1 > A2 > A4 0.33

A5 > A2 > A4 0.33

A2 > A4 0.33

A4 > A6 0.33


Slide 7.24

Frequent Trails Support = 0.1 and Confidence = 0.5

Trail Probability

A1 > A2 > A3 0.67

A5 > A2 > A3 0.67

A2 > A3 0.67


Slide 7.25

Pre-fetching and Caching Pages

• Learn access patterns to predict future accesses.

• Pre-fetch predicted pages to reduce latency.

• Can use Markov model and base the prediction on history of access.

• Also cache results of popular search engine queries.


Slide 7.26

E-commerce Click stream Analysis

• What is the user’s intention: browse, search or buy?

• Measure time spent on site - site stickiness

• Repeat visits – it has been shown that repeat visitors spend less time on the site; can be explained by learning.

• Measure visit-to-purchase conversion ratio, and predict purchase likelihood.


Slide 7.27

Supplementary Analyses to Improve eCommerce Web Sites

• Detecting visits from crawlers as opposed to human visitors.• Form error analysis, e.g. login errors, mandatory fields not filled,

incorrect format.• When and why do people exit the site, e.g. visitor puts item in cart

but exists before reaching the checkout.• Analysis of local search engine logs – correlate with site

behaviour.• Product recommendations based on association rules (people

who bought x also bought y).• Geographic analysis – where are the customers?• Demographic analysis – who are the customers?


Slide 7.28

Adaptive web sites

• Modify the web site according to user access.– Automatic synthesis of index pages (hubs that

contain links on a specific topic)– Based on a clustering algorithm that uses the

co-occurrence frequencies of pages from the log data.

– Finds a concept that best describes each cluster.


Slide 7.29

Trail Engine – Automating NavigationA Relevant Trail for the Query“mark research”

Mark

TeachingSCSIS

Staff Research WebTech


Slide 7.30

Trail Engine – Automating NavigationMarkov Chain Constructed from Search Engine Scores

Mark (1)

Teaching (3)SCSIS (2)

Staff (5) Research (3) WebTech (6)

0.6

0.4

0.66

0.4 0.6

0.34

1


Slide 7.31

Search Engine vs. Trail Engine

• A query is a conjunction of keywords.• A search engine returns pages containing all

the keywords.• A trail engine returns trails such that each

keyword appears in at least one page on the trail.

• So, a search engine is a special case of a trail engine.


Slide 7.32

Scoring Trails (I)

Mark (1) > Teaching (3)

• Average score: (1+3)/2 = 2

• Discounted sum (discount factor = 0.75):

1 + 3*0.75 = 3.25


Slide 7.33

Scoring Trails (II)

Mark (1) > SCSIS (2) > Staff (5) >

Mark (1) > Teaching (3)

• Sum distinct/no. pages:

(1+2+5+3)/5 = 2.2• Discounted sum (discount factor = 0.75):

1 + 2*0.75 + 5*0.75^2 + 1*0.75^3 + 3*0.75^4= 6.68


Slide 7.34

Redundancy in Trails

Mark > SCSIS > Staff > Mark

• Can remove last page in trail as it has already been visited.

Mark > SCSIS

• Is redundant with respect to

Mark > SCSIS > Staff


Slide 7.35

The Best Trail Algorithm

• Given a query we generate K starting points.

• We repeat the main computation M times for each starting point (as there is stochastic variation) .

• The algorithm is essentially a probabilistic best first algorithm.


Slide 7.36

Best Trail Algorithmic Detail

• Algorithm maintains a navigation tree, that keeps track of the trails explored.

• At each step we expand a link proportional to the score of the trail that is created by following the link.

• First explore then converge.


Slide 7.37

A Navigation Tree (Figure 7.11)Expanded according to the Markov chain’s probabilities

0:Mark

1:Teaching 2:SCSIS

3:Staff 4:Research

6:WebTech5:Mark

7:SCSIS


Slide 7.38

Best Trail – User Interface

• Figure 7.12 : Trail Search for query “knowledge technologies”


Slide 7.39


• Figure 7.13 : Nav-Search for query “knowledge technologies”


Slide 7.40


• Figure 7.14 : Visual Search for query “knowledge technologies”


Slide 7.41

Visualisation that Aids Navigation

• Visualisation of web site structure.

• Visualisation of web usage data.

• Visual search engines.


Slide 7.42

Web Site Maps

Hierarchical Site MapFigure 7.19

Graphical Site MapFigure 7.5


Slide 7.43

Directory Structures

Open directory categoriesFigure 7.16

Map of the Open DirectoryFigure 7.17


Slide 7.44

Categorised Site MapFigure 7.20


Slide 7.45

Query Specific Map for Web TechnologiesFigure 7.22


Slide 7.46

Fisheye Views

Figure 7.24 : Example of a star tree


Slide 7.47

Rapid Serial Visual Presentation

Figure 7.24 : RSVP browser on a small screen


Slide 7.48

Visualisation of User Trails in a Web Site

Figure 7.25 : VISVIP


Slide 7.49

Web Site Usage Visualisation

Figure 7.26 : Anemone


Slide 7.50

Visual Search Engines

Figure 7.28 : Grokker’s topic map for “beatles”


Slide 7.51

Visual Search Engines

Figure 7.29 : Kartoo’s topic map for “beatles”


Slide 7.52

Museum Experience Recorder

Figure 7.30 : Trail of a visitor to a museum

Documents

Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 7.1 Chapter 7 : Navigating the Web Frustration