© December 1999 George Paliouras, All Rights Reserved1 Learning Communities of Users on the...

© December 1999 George Paliouras, All Rights Reserved 1

Learning Communities of Learning Communities of Users on the InternetUsers on the Internet

George Paliouras

Christos Papatheodorou

Vangelis Karkaletsis

Constantine D. Spyropoulos

NCSR “Demokritos”

Email: [email protected]

WWW: http://www.iit.demokritos.gr/skel


Structure of the talkStructure of the talk

• Services on the Internet• Personalization of Internet services• Learning communities from usage data• Three case studies

– Information broker (filtering)– Digital library (retrieval)– Web-site (navigation)

• Conclusions


WWW: the new face of WWW: the new face of the Netthe NetOnce upon a time, the Internet was a forum for exchanging information. Then … …came

the Web.The Web introduced new capabilities …

…and attracted many more people …

…increasing commercial interest …

…and turning the Net into a real forum …


Services on the InternetServices on the Internet

Information providers are still the majority…

Commercial Non-Commercial

CNN Reuters

Times Yahoo

CORDIS NCSTRL

MLNET Library


• We have looked at three different types:– Information filtering (profiles)– Information retrieval (queries)– Navigation

Information accessInformation access

• The Web has introduced new ways to access information.

passive

active

• … covering the majority of today’s information services.


Personalized information Personalized information accessaccess

• Adaptation of the system to the user.

• Social motivation:– Better service for the citizen (reduction of

the information overload).

• Commercial motivation: – Customer relationship management

(targeted advertisement, customer retention, increased sales, etc.)



“The Quantity of People Visiting Your Site Is Less Important Than the Quality of

Their Experience”

Evan I. Schwartz, Webonomics, Broadway Books, 1997



sources

server

receivers


User modelingUser modeling

• The process of constructing models that can be used to adapt the system to the user’s requirements.

• Types of user requirement:– Interests (e.g. sports and finance articles)– Knowledge level (e.g. novice – expert)– Preferences (e.g. appearance of GUI)– etc.


User ModelsUser Models

• User model (type A): [PERSONAL]

User x -> sports, stock market

• User model (type B): [PERSONAL]

User x, Age 26, Male -> sports, stock market

• User community: [GENERIC]

Users {x,y,z} -> sports, stock market

• User sterotype: [GENERIC]

Users {x,y,z}, Age [20..30], Male -> sports, stock market


Machine Learning / Data Machine Learning / Data MiningMining

• Acquisition of models from usage data.

• Types of learning– Supervised learning: requires manually

tagged examples.– Unsupervised learning: clusters untagged

examples, according to similarity.


Learning user modelsLearning user models

User 1 User 2 User 3 User 4 User 5

Observation of the users interacting with the system.

User models

Community 1 Community 2 User communities


Collaborative filteringCollaborative filtering

• Memory-based “learning”, (e.g. k-nn):– Given a group of users…– …and a new user…– …find similar users.

• Already in commercial use (e.g. Firefly, amazon.com)

• Problem: It does not give any insight about the usage of the system.


Clustering users into Clustering users into communitiescommunities

• Clustering methods:– Conceptual clustering

(COBWEB, ITERATE)– Graph-based clustering (Cluster mining)– Statistical clustering (Autoclass)– Neural clustering (Self-organising Maps)


Conceptual clusteringConceptual clustering

• COBWEB generates a hierarchy of concepts.

• Each concept is a cluster of objects.

• Our concepts are the communities.

• Our objects are “user models”.

• Similarity metric: category utility.

• Each user in only one community.


Meaningful communitiesMeaningful communities

• Question: What are the characteristics of a community?

• Answer: Community characterization, measuring frequency increase.

• Example: How frequently do users of the community read sports news, compared to the whole set of users.


Cluster miningCluster mining

• Searches for cliques in a graph of the following form:

hardware

mathematics of computing

software

computingmilieux

computingmethodologies

0.22

0.12 0.27

0.19

0.13

0.024

0.03

0.04

0.040.04 0.02

0.030.014 0.0262

0.02


Cluster miningCluster mining

• Nodes: features in the user model.

• Edge labels: frequency at which the two nodes appear together in the data.

• Edge reduction: using a threshold.

• Clique: commonly met pattern in the behavior of the users.

• Each user in several communities.


Case studiesCase studies

• Information broker (filtering)

• Digital Library

(retrieval)

• Web-site

(navigation)ACAI99

NCSTRL

?


Criteria for the Criteria for the communitiescommunities

• We evaluate the quality of community descriptions (behavioral patterns), by:– Coverage: Proportion of characteristics

appearing in the descriptions.– Overlap: Extend of overlap between

descriptions:– Meaningfulness: Do the descriptions make

sense? Are they interesting?


I: Profile-based filteringI: Profile-based filtering

• User models: profiles of news categories for each user.

• User communities: users with common news-reading interests.

• Community descriptions: news categories for each community.


I: COBWEBI: COBWEB

A (1078)

B (681)C (397)

D (328) E (353) F (98)G (181) H (118)

J

(104)

K

(161)

L

(95)

M

(102)

N

(156)

O

(38)

P

(17)

Q

(43)

R

(36)

S

(96)

I

(63)

W

(28)

V

(62)

U

(28)

T

(49)

Community hierarchy


0

0,2

0,4

0,6

0,8

1

0 0,5 1pruning parameter

cove

rage

cobweb (level 2)cobweb (level 3)

I: COBWEBI: COBWEB

Coverage Overlap

012345678

0 0,5 1pruning parameter

over

lap

cobweb (level 2)cobweb (level 3)


I: COBWEBI: COBWEB

D

E Internet (0.55)

F Economic ind. (0.73), Economics & Finance (0.68), Computers (0.6), Transport (0.53), Financial ind. (0.5)

G Economic ind. (0.58), Economics & Finance (0.61)

H Computers (0.53)

Community descriptions


I: Cluster miningI: Cluster mining

Behavioral patternsTelecom, Computers, Internet, Industries, Economics/Finance

Telecom, Computers, Networks

Telecom, Economic ind., Economics/Finance

Hardware, Software

Financial ind., Economic ind., Economics/Finance

Financial ind., Economic ind., Financial markets

Sport, Entertainment electronics


I: ComparisonI: Comparison

012345678

0 0,5 1Connectivity threshold (cluster mining)

and pruning parameter (COBWEB)

Ove

rlap

cluster mining

COBWEB(level 2)


II: Query-based retrievalII: Query-based retrieval

• User models: processed queries.

• User communities: user queries with common keywords.

• Community descriptions: characteristic keywords for each community.

• Pre-processing:– Lemmatization and synonyms (WordNet).– Generalization to top ACM categories.


II: COBWEBII: COBWEB

Community descriptionsComputer Systems Organisation (1.0)

Software (1.0)

Hardware (1.0)

Information Systems (1.0), Computing milieux (0.63), Computing methodologies (0.28)

Information Systems (1.0)

Computing methodologies (1.0), Hardware (1.0)

Computing methodologies (1.0), Software (1.0)

Computing methodologies (1.0)


II: Cluster miningII: Cluster mining

Behavioral patternsHardware, Software, Computing Milieux, Computing Methodologies

Hardware, Software, Computing Milieux, Maths of Computing

Hardware, Computer Systems Organisation

Theory of Computation, Maths of Computing

Information Systems, Software, Computing Milieux, Computing Methodologies

Information Systems, Software, Computing Milieux, Maths of Computing


III: Web-site navigationIII: Web-site navigation

• User models: access sessions as sets of pages or sets of page transitions.

• User communities: users with common navigation behavior.

• Community descriptions: Pages or page transitions for each community.

• Pre-processing: – Sessions from access logs. (duration)– Dimensionality reduction, by feature selection.


III: COBWEBIII: COBWEB

Community descriptions24>25, 23>24, 1>24, 1>19, 19>23

1>22, 22>20, 20>31, 31>27, 27>7, 19>23

22>31, 1>22

22>27, 1>22

1>30

1>30, 8>1, 1>8

30>31, 1>30


III: Cluster miningIII: Cluster mining

Behavioral patterns

1>19, 19>23, 23>24, 24>25

1>24, 24>25

1>22, 22>31

1>22, 22>20

1>30, 30>31

22>20, 20>31, 31>27

22>20, 20>27

1>8, 8>1

1>9, 9>2

20>31, 31>27, 27>7

19>23, 23>14

23>14, 27>7

1>2, 2>11

2>11, 11>12

1>23, 23>24


ConclusionsConclusions

• Community construction gives insight about the usage of information services.

• Unsupervised learning can do the job.

• Characterization makes the results useful.

• Substantial data engineering is need for different types of information access.


A paradoxA paradox

High commercial demand for a research product!

Solutions need to be simple and efficient!

© December 1999 George Paliouras, All Rights Reserved1 Learning Communities of Users on the...

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