Information Foraging & Information Scent: Theory, Models, and Applications

Information Foraging &

Information Scent:

Theory, Models, and Applications

Peter Pirolli

User Interface Research

Work supported in part by the Office of Naval Research

Aim of this Talk

• Overview – Information foraging theory – Information scent

• Sample of psychological investigations• Sample of applications

Webpages

Database

Server

Human-Information Interaction:Can approach from user or producer side

Overview

• Motivations, origins, assumptions• Initial development: Scatter/Gather use• Extension: WIF-ACT model of WWW use• Information scent as a critical parameter

of the large-scale shape of WWW use

Motivations & origins• Humans are informavores (George Miller, 1983)

– Organisms that hunger for information about the world and themselves

• Humans seek, gather, share, and consume information in order to adapt

Pressures of the information environment

Journals/People x106

Year

0.01

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1000

10000

100000

1000000

1750 1800 1850 1900 1950 2000

Journals

Growth of available information

Source: Price (1963)


YearYear

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1000000

1750 1800 1850 1900 1950 2000

Capacity of human working memory

Growth in attention


Herbert A. Simon

A wealth of informationcreates a poverty of attention and a need to allocate it efficiently

“”

WWW challenges HCI theory

• 2003 e-commerce revenue = $1 Trillion (est.)

BUT• 65% of virtual shopping trips end in failure

(Souza, 2000)

• 1M site visitors, 40% do not return, cost=$2.8 M (Manning, 1998)

• WWW site redesigns = $1.5 M/yr to $2.8 M/yr (Manning, 1998)

Information Foraging Theory

• Take concept of informavores seriously• Key ideas

– Information scent. Local cues used to explore and search information spaces

– Economics of attention and the cost structure of information

– Optimal foraging models

Take concept of informavores seriously

• Information processing systems evolve so as to maximize the gain of valuable information per unit cost– Sensory systems (vision, hearing)– Information access (card catalogs, offices)

• Natural selection has made animals (and our human ancestors) very good at searching for food (foraging)– Modern information foragers use problem-solving

abilities with deep evolutionary roots in food foraging

information valuecost of interaction[ ]maximize

Time scales of analysis

Time scale (s)Psychologicaldomain

10-1000 •Problem solving•Decision making

1-100•Visual search•Motor behavior

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email: [email protected] This page updated December 18, 2000.

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.100-1•Visual attention•Perceptual judgment

User Interface Domain

Example: Scatter/Gather

• Information scent• Optimal foraging analyses• ACT-IF cognitive model• Evaluation by user simulation



information scentTokyo

San Francisco

New York Cues that facilitate orientation, navigation, assessment of information value

Scatter/Gather

• supports exploration/browsing of very large full-text collections (~ 1,000,000)

• creates clusters of content-related documents• presents users with overviews of cluster

contents• allows user to navigate through clusters and

overviews• More recently extended to multi-modal

Scatter/Gather (Chen et al., 1999)– Images + text

Scatter/Gather task

Scatter/GatherWindow

Law

World News

AI

CS

Medicine

Nat. Lang.

Robots

Expert Sys

Planning

Bayes. Nets

Display TitlesWindow

information scent

cell

patient

dose

beam

new

medical

treatments

procedures

InformationNeed

Text snippet

• Spreading activation– Derived from models of human memory– Activation reflects likelihood of relevance

given past history and current context– Approximates Bayesian network

0

1

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6

7

1 2 3 4 5 6 7 8 9 10

Rank

Pro

bab

ilit

y r

ele

van

t Observed Rating

Predicted Rating

spreading activation

Bi = ln( ) Pr(i)Pr(not i)

Sji = ln( ) Pr(j|i)Pr(j|not i)

i“bread”

j“butter”

Ai = Bi + WjSji

Activation of node i

Base-levelactivation

Activation spreadfrom linked nodes j

Base-level reflects likelihood of occurrence

Strength of link spread reflects likelihoodof cooccurrance

spreading activation networks(for modeling “scent”)

Documentcorpus

Wordstatistics

Spreadingactivationnetwork

interface provides good scent of underlying document clustering

0

0.05

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0 1 2 3 4 5 6 7 8 9 10

Rank of Cluster (c)

Pro

port

ion

of

Rele

van

t D

ocu

men

tsdP(c)

dD(c)Identified by computerPerceived by model

Summary: Information Scent

• Spreading activation predicts user judgments

• Networks built a priori. Only need to estimate one scaling parameter from user data

• Can be used to assess “goodness of links”



cost/value estimates• TREC

– queries and expert-identified relevant documents

• Analysis of clustering algorithm– distribution of relevant information over

clusters– Time costs

foraging evaluations

enrich

exp

loit

choose cluster

total relevant documents

task time

RD =

t1 + N t2

Activation from cluster text

Number of relevant documents in clusterTime to process cluster

Time to process relevant docsTime to process all docs

RSG = RD at t + 1

Cluster selection (optimal diet model)

Rank profitability

Rel

evan

t do

cum

ents

/sec

ond

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5 6 7 8 9 10

Number of relevant documents in cluster

Time to process cluster =

Total relevant documentsTotal timeR =

R

Optimum

Choose clusters (in descending rank ) if > R

enrichment v exploitation

0 200 400 600 800 10000

.01

.02

.03

.04

.05

.06

R*SG

R*D

Time (sec)

Rat

e o

f ga

in

R*SG > R*

D R*D > R*

SG

relevant documentstime cost

if user chooses to display clusters now

if user choosesto display later(after more Scatter/Gather)

R=



Model-Tracing Method

System UsersCognitive

Model

Trace

Psychology

Optimal foragingtheory

ACT-IF production system

Declarative Memory Procedural Memory

Condition -> Action

Condition -> Action

Condition -> Action

Condition -> Action

PerceptualInput

MotorOutput

Foragingevaluationheuristics

production rule evaluationsSELECT-RELEVANT-CLUSTERGoal is to Process Scatter/Gather Window& there is a Query& there is an unselected cluster

Select the cluster

DO-SCATTER/GATHERGoal is to Process Scatter/Gather Window& there is a Query& some clusters have been selected

Scatter/Gather the window

DO-DISPLAY-TITLESGoal is to Process Scatter/Gather Window& there is a Query& some clusters have been selected

Scatter/Gather the window

RSG

RD

Model predicts user action

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9

10

More

Rank of Predicted Production

Fre

qu

en

cy



Evaluation by user simulation

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50

Few ManyPerc

en

t C

han

ge f

rom

Baselin

e(R

ele

van

t D

ocu

men

ts)

ImprovedClustering

FasterInteraction

0

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Soft Hard

Task Deadline ConditionRepository Results Relevant to Task

ImprovedClustering

FasterInteraction

Summary: Scatter/Gather

• ACT-IF model matches user behavior• (most of) Model specified a priori• People optimize value/cost using

foraging heuristics

Overview



WIF-ACT

• Web Information Foraging - ACT• Not a reality yet

– Preliminary version interacts with Internet Explorer

• What we have done:– Specialized instrumentation– Methodology– Preliminary analysis of information foraging and

information scent

Instrumentation

WebLogger

Eye trackerPoints of regard

Event logWebEyeMapper

Interface objects

Fixation table

Cached pages

Database& statistics

Visualizations

WebLogger

Eye trackerPoints of regard

Event logWebEyeMapper

Interface objects

Fixation table

Cached pages

Database& statistics

Visualizations

WebLogger Event File

(BEFORE-NAVIGATE (http://altavista.com/ ) 105.331s 0.100s 951763010 10:36:50)(DOC-MOUSEMOVE (881 122 ) 105.431s 0.100s 951763010 10:36:50)(NAVIGATE-COMPLETE (http://www.altavista.com/)105.632s 0.201s 951763011 10:36:51)(EYETRACKER-SYNC (103 ) 106.242s 0.610s 951763011 10:36:51)(DOCUMENT-COMPLETE (http://www.altavista.com/)106.773s 0.531s 951763012 10:36:52)(SCROLL-POSITION (0 0 759 1181 ) 106.853s 0.080s 951763012 10:36:52)(DOC-MOUSEMOVE (874 123 ) 107.024s 0.171s 951763012 10:36:52)(DOC-MOUSEMOVE (874 123 ) 107.044s 0.020s 951763012 10:36:52)(DOC-MOUSEMOVE (874 123 ) 107.214s 0.170s 951763012 10:36:52)(EYETRACKER-SYNC (104 ) 107.244s 0.030s 951763012 10:36:52)(CHAR (a 874 123 ) 108.125s 2.904s 951763013 10:36:53)(EYETRACKER-SYNC (105 ) 108.245s 1.001s 951763013 10:36:53)(DOC-KEYPRESS (a INPUT ) 108.446s 0.201s 951763013 10:36:53)

Study• 6 “Find information” tasks, e.g.,

– “You are Chair of Comedic events for Louisiana State University in Baton Rouge. Your computer has crashed and you have lost several advertisements for upcoming events. You know that the Second City tour is coming to your theatre in the spring, but you do not know the precise date. Find the date the comedy troupe is playing on your campus. Also find a photograph of the group to put on the advertisement.”

• 12 Stanford University students• 2 tasks (CITY, ANTZ) analyzed for 4 participants

Analysis

• Task/Information environment– Information patch structure– Problem space structure

• Information scent

Information structure

• Web sites– Portals– Search engines

• Pages– Website home page– Search engine page– Hitlist page

• Content elements

YahooMovie Posters Archive

123 Posters

Problem space structure

– URL

– Link

– Keyword

– Visual Search

123 Posters AntzS

www.antz.com www.antz.com/antzstoreCLCL

www.antzthemovie.com www.google.comTUTU

Web Behavior GraphS1 S2 S3 S4 S6

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State in Problem Space

Hit List

Web Behavior Graph

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Execution of Operator

Return to Previous State

Web Behavior Graph

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Link Problem Space

URL Problem Space

Keyword Problem Space Visual Search

Problem Space

Web Behavior Graph

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No Scent

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Web Behavior Graphs (WBGs)

Information scent & leaving a site

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0.5

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1.5

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2.5

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Last-4 Last-3 Last-2 Last-1 Last AvgNext

Page visit at site

Sce

nt

rati

ng

3-Visits (N =3)

5-Visits(N =6)

Patch-leaving policy:

Leave Web site when information scent goes below some threshold

WWW Study Summary

• “Patchy” structure of the WWW– Goals and behavior organized around information

patches

• Multiple problem spaces• WBGs reveal heuristic search

– Easy search could be facilitated by making operations more efficient

– Hard (heuristic) search could be facilitated by better heuristic information (i.e., information scent)

• Information scent controls significant amount of behavior

Overview



Usability gurus tout information scent key to good WWW design

UI research on better link summaries

• Enhanced Thumbnails (Woodruff et al., 2001)

• Picture thumbnails + emphasized text relevant to query or helpful in assessing page

0

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120

Text Plain Enhanced

Tota

l Sea

rch

Tim

e (s

)

Note: N = 12

Information scent and large-scale Web use regularities

• Information scent, stickiness, and the universal law of surfing

• Phase shifts in cost structure of surfing

0

4,000

8,000

12,000

16,000

20,000

0 5 10 15 20 25 30 35 40 45 50

Clicks (L)

Fre

qu

en

cyInverse Gaussian Distribution

L

L

LLP

2

3

3 2exp

2)(

Variance(L) = 3/

GVU Website

surfers at each level

1

10

100

1000

1 10 100 1000

log(Clicks) log(L)

log

(Fre

qu

ency

)

slope -3/2

2og

2)(

og23

)(og 2

2

lL

LLlLPl

Surfing as Brownian motion(or real option)

• V is expected value• L is the number of clicks

L varies as independent

and identically distributed Gaussian

• Surfing proceeds until the perceived cost is larger than the discounted expected future value (VL hits a threshold)

VL = VL-1 + L

• Mean rate & variance of movement towards threshold

• High scent pages likely to lead to high scent; low scent to low scent

(Huberman, Pirolli, Pitkow, & Lukose, 1997)

Phase shifts in search costs

• “Thumbnails are better because you don’t have to waste time reading words”

• Implies that speed is the important factor• BUT: Improvements in accuracy have

qualitative impact

Information scent and the cost of search(based on Hogg & Huberman, 1987)

D = depth of search hierarchy

z = average branching fraction

(1- q) = prob. of eliminating wrong branches

(q, z) = qz =average no. branches explored

A(U, q, z) = average no. nodes explored within distance U= (1- (q, z)U +1)/(1-(q, z))

N(D, z, q) = average no. nodes examined before desired goal found

= (z - 1)q2[ ] A(s - 1, q, z)

s=1

D-1

Phase shifts in search regime due to information scent

1 2 3 4 5 6 7 8 9 100

250

500

q=1q=.25

q=.15

q=.05q=.01

NodesExamined

Number of Levels (D)(z = 10)

Information scent controls

• Stickiness• Search cost regime

Applications

• Simulated user– Web page usability– Web site usability

• Other– Prefetching/caching to improve access

speed– Improve search engine results– Dynamic optimization of page and site layout

Web User Flow by Information Scent

Web site

WebPage

contentlinks

Web user flow simulation

UserInformation

goal

Predictedpaths

inferring user goal

Web site

WebPage

contentlinks

Web user flow simulation

UserInformation

goal

observedpaths

Conclusion: Information foraging & Information scent

• How & why humans adapt to information environments

• Formal models mix mathematics of optimal foraging theory with computational cognitive models

• Applications to UI design, Web access, Web usability analysis

• Proper theory for the study of human informavores

Information Foraging & Information Scent: Theory, Models, and Applications

Documents

information environmentherbert

information interaction

information environmentsource

simona wealth of information

gain of valuable information

user simulationexample

www site redesigns

cognitive modelevaluation