E6895 Advanced Big Data Analytics Lecture 6: Social and ...cylin/course/bigdata/EECS6895-AdvancedBig... · 1091-1092 (2009). Jyh-Ren Shieh, Yang-Ting Yeh, Chih-Hung Lin, Ching-Yung

© CY Lin, 2016 Columbia UniversityE6895 Advanced Big Data Analytics — Lecture 6

E6895 Advanced Big Data Analytics Lecture 6:

Social and Cognitive Analytics (II)

Ching-Yung Lin, Ph.D.

Adjunct Professor, Dept. of Electrical Engineering and Computer Science

IBM Chief Scientist, Graph Computing, Watson Research Center

© CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics — Lecture 6 2

What keywords should I put in the search box to get the information I really want? Q.

Relational Term-Suggestion

© 2016 Columbia University

Term Suggestion and Query Expansion

Log-based

Multi-partite Network Analytics

WordNet WikipediaInfluenced by test collection characteristics

Simple concept links only

Limited semantic relatedness

Difficult to update

Extracting human factor

Incorporate expertise

Network community -based

Click log, biased in favor of top ranks

Query log, failure for rare queries

Document-based

Ontology-based

Multi-partite network analytics

Not publicly available


■ Influenced by test collection characteristics ■ No consideration of key terms that are highly

semantically related but do not frequently co-occur.

■ Influenced

Document-based

apple juice apple tree

apple store apple TV

Kim, M. AND Choi, K. A. 1999. Comparison of collocation-based similarity measures in query expansion. Information Processing and Management 35 (1999), 19-30.



Log-based





Difficult to update






Document-based

Ontology-based




Log-based ■ Cluster queries with similar clicked URLs

■ Identifying the mapping between queries and clicked URLs

Pet food

Dog food

BAEZA-YATES, R., AND TIBERI, A. 2007. Extracting Semantic Relations from Query Logs. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), 76-85.



Log-based





Difficult to update






Document-based

Ontology-based




WordNet as Ontology

■ Manuallyconstructed system based on individual words benefit will be limited

■ System is not easily updated

Pedersen, T, Patwardhan, S and Michelizzi, J. "WordNet::Similarity - Measuring the Relatedness of Concepts" 2004 In Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI-2004) pp. 1024-1025.


Wikipedia as Ontology


■ Wikipedia is a web-based free encyclopedia that anyone can edit.

■ The English Wikipedia edition

■ 2.4 million articles

■ 1 billion words.

■ Wikipedia relies on the power of collective intelligence

■ by peer-reviewed approaches rather than the authority of individual.

■ high quality,

■ almost noise free.



Previous Approaches

■ Merely as an online dictionary and utilize it only as a structured knowledge database

■ Using associated hyperlinks

MILNE, D., WITTEN, I. H., AND NICHOLS, D. 2007. A Knowledge-Based Search Engine Powered by Wikipedia. In Proceedings of the 16th ACM Conference on Information and Knowledge Management (CIKM 2007), 445-454..



Log-based





Difficult to update






Document-based

Ontology-based


W 2.0Not publicly available


Log-based




Difficult to update




Ontology-based


Crawling is resource-intensive

Human factor modeling

Semantic relatedness difficult to evaluate

Multi-partite Network AnalyticsTerm Suggestion and Query Expansion

Document-based

Our Challenge

© 2016 Columbia University6/3/12 15



Query

Contributor Expertise Analysis

Optimization

Relative Importance Ranking

Visualization Interface

Evaluation Interface

Ontology Data Sampling

Semantic Relatedness Weighting


C C

C:contributors T:Terms

T

T

C C

Key Term

T

C C

T

C C

L

L L

L

L:Categories

Layer by layer


Query


Optimization








Query


Optimization








Contributor Expertise factor

Expertise inference

Expertise

Contributor to contributor

Contributor to categories

Term to categories

Term to Term


Query


Optimization







High Semantic Relatedness Term Suggestion from Our System


Word-completion Term Suggestion


P@1 P@5 S@5 S@20 MRR

Simple link 0.3736 0.3039 0.6017 0.6231 0.4023

+Contributor 0.6151 0.3917 0.8031 0.8116 0.4125

+Expertise 0.6693 0.4412 0.8297 0.9620 0.5919

Performance Comparison for Different Relationship Levels. Using BibSonomy Dataset

Experiment I


Wordnet Bag of words Our algorithm

Literature 62.0% ± 5% 62.7% ± 4% 76.8% ± 6%

Natural science 60.7% ± 4% 65.6% ± 6% 73.3% ± 3%

Sociology 72.1% ± 5% 62.9% ± 5% 72.5% ± 7%

Business 60.4% ± 6% 58.5% ± 8% 67.1% ± 7%

Law 52.2% ± 9% 50.4% ± 8% 66.3% ± 6%

Engineering 54.0% ± 6% 68.3% ± 5% 66.2% ± 4%

Electrical & Computer Eng.

77.0% ± 4% 68.0% ± 3% 82.3% ± 3%

Life Science 73.1% ± 6% 70.9% ± 6% 81.4% ± 7%

Agriculture 72.6% ± 5% 65.1% ± 6% 72.3% ± 5%

Medical 63.0% ± 8% 65.6% ± 7% 61.6% ± 8%

ODP-based precision evaluation results increase 12.5% in average

Experiment II – Accuracy on different categories


Synonyms Hyponymy Antonyms Paraphrase

Zhao et al. - - - 0.7444

Our approach 0.2197 0.3665 0.2313 -

Precision Comparison With Paraphrase Detection System

82% of the suggested terms are reported as related, i.e., synonyms (22%), hyponyms (37%) or antonyms (23%)

© CY Lin, Columbia UniversityE6895 Advanced Big Data Analytics — Lecture 6 28

References

Jyh-Ren Shieh, Ching-Yung Lin, Shun-Xuan Wang, Ja-Ling Wu, “Relational Term-Suggestion Graphs Incorporating Multi-Partite Concept and Expertise Networks,” ACM Transactions on Intelligent Systems and Technology (2012).

Jyh-Ren Shieh, Ching-Yung Lin, Shun-Xuan Wang, Ja-Ling Wu, “ Building Multi-Modal Relational Graphs for Multimedia Retrieval,” International Journal of Multimedia Data Engineering and Management (IJMDEM): pp. 19-41 (2011). Best paper award nomination.

Jyh-Ren Shieh, Yung-Huan Hsieh, Yang-Ting Yeh, Tse-Chung Su, Ching-Yung Lin, Ja-Ling Wu, “Building term suggestion relational graphs from collective intelligence,” World Wide Web Conference (WWW 2009) pp. 1091-1092 (2009).

Jyh-Ren Shieh, Yang-Ting Yeh, Chih-Hung Lin, Ching-Yung Lin and Ja-Ling Wu, “Using Semantic Graphs for Image Search,” IEEE International Conference on Multimedia & Expo (ICME 2008), pp. 105-108 (2008).

29

Recommendations – Information Overload

à Provide users with the information they need at the right time

Books, Journals,Research

papers

News

Books

Movies

Electronics

30

Outlinep Introduction and Background p Exploiting Dynamic Patterns for

Recommendation Systems ■ Community based Dynamic Recommendation ■ Personalized Recommendation Driven by

Information Flow p Other Related Work p Conclusions

31

Recommender Systems: General IdeaUser profile (info about the user)

Set of itemscomparison

Recommendation e.g. news articles, books, music, movies, products, …

Sample applications p E-commerce

Product recommender - Amazon p Enterprise Activity Intelligence

Domain expert finder, … p Digital Libraries

Pages/documents/books recommendation p Personal Assistance Museum guidance, …

Algorithms p Content-based Filtering p Collaborative Filtering p Hybrid Filtering (combination of the above two)

32

Preference: and

Content-based Filtering (CBF)Recommending items based on the content and properties

sports World HealthWorld Health…TechMatching

Item Stream

Recommending

33

Collaborative Filtering (CF)Leveraging opinions of like-minded users

People with similar tastes

Given

Reco

mm

end

34

Exploiting Dynamic Patterns for Recommender Systems

p Dynamic nature from both items and users ■ Items expire over time with types of

p Short-term p Long-term

■ Users’ intentions of p Updating breaking information p Looking for long-term items

■ Users’ interests evolve over time

p User adoption patterns ■ Some users are earlier adopters

35


Recommendation Systems ■ Community based Dynamic Recommendation [Song

et al. SDM’06] ■ Personalized Recommendation Driven by

Information Flow [Song et al. SIGIR’06] p Other Related Work p Conclusions

36

Community based Dynamic Recommendationp Objective

■ Push personalized relevant information to users at the right time

p Challenges ■ Information should be personalized ■ Users’ needs are changing and evolving

p Our Novel Technology Solutions -- Adaptive User and Community Modeling ■ Content-Time-Community (CTC) model: a Bayesian

Network infrastructure for finding evolving communities and content topics

■ Time-Sensitive Adaboost model: a scheme to decide the relevance of information based on dynamic personal/community profiles

37

Scheme Overview

Dynamic recommendation is addressed by dynamic pattern analysis, content analysis and community construction

Dataset includes user log files and users’ and documents’ information

Community based

recommendation

Content analysis

Community construction

Dynamic pattern analysis

Dataset

New items

Recommendations

38

Dynamic Pattern Analysisp Items’ perspective

■ Short-term and long-term Item detection ■ Expiration date prediction ■ Popularity identification ■ Freshness identification

p Users’ perspective ■ User intention detection

p Updating short-term items p Looking for long-term items

39

Item Access Patterns

Peak on one day followed by a decay -- short-term documents

Sustained interest -- long-term documents

Doc ID = '289078253'

0

50

100

150

200

1 21 41 61 81 101 121 141 161 181 201 221 241

Time(days)

Acc

ess

times

Doc ID = '10736738'

0

2

4

6

8

10

1 21 41 61 81 101 121 141 161 181 201 221 241Time(days)

Acc

ess

times

announcements, conference notes, and executive summaries

handouts, specifications, and reference documents

verification

40

More Item Dynamic Patternsp Expiration Date Prediction

■ Observations p The longer nobody downloads a document, the less

likely it will be downloaded in the future where

p Popularity identification ■ how many times one document was downloaded by

others during a time period

p Freshness identification ■ difference between user’s login time and the disclosure

time of the document

( )( ) 1 expP e tη= − − ! if 0 others

na t na tt thr t thrt

− >"= #$

!

41

User Intention Detection

Higher-level managers access short-term documents

Other employees access long-term documents

From Yusuf

Job Titles and Access Trends

0%10%20%30%40%50%60%70%80%90%

100%E

xecu

tive

Div

isio

n H

ead

Vice

Div.

Hea

d

Chi

ef M

anag

er

Vice

Dep

t. H

ead

Gen

eral

Man

ager

Man

ager

Dep

t. H

ead

Bra

nch

Hea

d

Tech

. Man

ager

Gro

up M

anag

er

Man

ager

-1

Sal

es M

anag

er

Sen

ior M

anag

er

Pro

ject

Man

ager

Man

ager

-2

Sys

tem

Man

ager

Pro

ject

Man

ager

Acc

ess

to s

hort

-term

Doc

umen

ts

à Users’ intentions are related to their job titles

42The probability of the word

under the jth topicThe probability of choosing a word from

the jth topic in the current document

river

TOPIC 2

river

riverstreambank

bank

stream

loan

TOPIC 1

money

loanbank money

bank ba

nk

loan

DOCUMENT 2: loan1 river2 stream2 loan1 bank2 river2 bank2

bank1 stream2 river2 loan1 bank2 stream2 bank2 money1 loan1 river2 stream2 bank2 stream2 bank2 money1 river2

DOCUMENT 1: money1 bank1 bank1 loan1 river2 stream2 bank1

money1 river2 bank1 money1 bank1 loan1 money1 stream2 bank1 money1 loan1 river2 stream2 bank1 money1

0.3

0.8

0.2

Content Analysis - Latent Dirichlet Allocation (LDA) [Blei et al. 2003]

( )djθ

( )jwφ

0.7

Goal – categorize the documents into topics p Each document is a probability distribution over topics p Each topic is a probability distribution over words

( ) ( ) ( )1

|T

i i i ij

P w P w z j P z j=

= = =∑The probability of ith word in a given document

Mixture components

Mixture weights

iw

43

INPUT: ■ document-word counts

• D documents, W words

OUTPUT: ■ likely topics for a document

LDA (cont.)

( ) ( )( | ) |P z w P w z P z∝θ

wW

D

β

α

z

T

T: number of topics

φ

: Observations

Bayesian approach: use priors Mixture weights ~ Dirichlet( α ) Mixture components ~ Dirichlet( β )

p Parameters can be estimated by Gibbs Sampling

p Outperform Latent Semantic Analysis (LSA) and Probabilistic LSA in various experiments [Blei et al. 2003]

44

Traditional Content Clustering

fw2

fwj : the frequency of the word wj

in a document

fw1

fw3 Clustering: Partition the feature space into segments based on training documents. Each segment represents a topic / category. (ç Topic Detection)

Hard clustering: e.g., K-mean clustering

1 2{ , ,..., }

Nw w wd f f f z= →

w1

: observationsz1Topics

Words

z2 z3 z4 z5

w5w2 w3 w4 w6Another representation of clustering (w/o showing the deterministic part)

( | )P Z wW = f

Soft clustering: e.g., Fuzzy C-mean clustering

w1


Words

z2 z3 z4 z5

w5w2 w3 w4 w6

Another representation of clustering

d1Documents d5d2 d3 d4 d6

45

Traditional Content Clustering

fw2


in a document

fw1

fw3 Clustering: Partition the feature space into segments based on training documents. Each segment represents a topic / category. (ç Topic Detection)

Hard clustering: e.g., K-mean clustering

1 2{ , ,..., }

Nw w wd f f f z= →

w1


Words

z2 z3 z4 z5

w5w2 w3 w4 w6Another representation of clustering (w/o showing the deterministic part)

( | )P Z wW = f

Soft clustering: e.g., Fuzzy C-mean clustering

46

Content Clustering based on Bayesian Network

( | )P W Z

Bayesian Network: • Causality Network – models the causal relationship of attributes / nodes • Allows hidden / latent nodes

Hard clustering:

w1 : observations

z1Topics

Words

z2 z3 z4 z5

w5w2 w3 w4 w6

soft clustering

d1Documents d2 d3

( | ) ( )( | )( )

P Z W P WP W ZP Z

=

( | )P Z D

( ) argmax ( | )z

h D d P Z= = wW = f

( )h Dhard clustering

s. c.

<= MLE

<= Bayes Theorem

47

Content Clustering based on Bayesian Network – Hard Clustering

w1

: observations

z1Topics

Words

z2 z3 z4 z5

w5w2 w3 w4 w6

( | ) ( ) ( | ) ( )( | )( ) ( | )

P Z W P W P Z W P WP W ZP Z P Z W dW

= =∫

z

w

N: the number of words (The number of topics (M) are

pre-determined)Major Solution 1 -- Dirichlet Process: • Models P( W | Z) as mixtures of Dirichlet probabilities • Before training, the prior of P(W|Z) can be a easy Dirichlet (uniform distribution). After training, P(W|Z) will still be Dirichlet. (ç The reason of using Dirichlet)

Major Solution 2 -- Gibbs Sampling: • A Markov chain Monte Carlo (MCMC) method for integration of large samples ➔ calculate P(Z)

z

wN

βM

φ

Topic-Word distributions

Latent Dirichlet Allocation (LDA) (Blei 2003)

shown as

48

Content Clustering based on Bayesian Network – Soft Clustering

w1

: observations

z1Topics

Words

z2 z3 z4 z5

w5w2 w3 w4 w6

N: the number of words A: the number of docs

shown as

d1Documents d2 d3

w

d

z

LDA (Blei 2003)β

MφTopic-Word

distributions

α θ

wN

d

A

zA

Document-Topic distributions

49

Comparison of Dirichlet Distribution with Gaussian Mixture Models (1)p Dirichlet Distribution: 1 21 1 1

1 2 1 1 2 1 2

1

( )( , ,..., ; , ,..., ) ...( )

ra a ar r rr

kk

Nf f f a a a f f fa

ρ − − −−

=

Γ=

Γ∏0 1kf≤ ≤

1

1r

kkf

=

=∑

p Multivariate Gaussian:2 2 2( ) ( ) ( )1 1 2 2 1 1

2 2 21 2 1

1 2 1 1 1 2 2 1 1 11

1

1( , ,..., ; , , , ,..., , ) ...(2 )

f f fr r

rr r r r

rk

k

f f f e e eµ µ µ

σ σ σρ µ σ µ σ µ σπ σ

− − −− −

−

− − −

− − − −−

=

=

∏

50

Beyond Gaussian: Examples of Dirichlet Distribution

51

Use Dirichlet Distribution to model prior and posterior beliefs p Prior beliefs: p E.g.: *fair* coin?

■ Flipping a coin, what’s the probability of getting ‘head’.

( ) ( ; , )f beta f a bρ =

beta(1,1): No prior knowledge beta(3,3): prior knowledge -- this coin may be fair

52

Use Dirichlet Distribution to model prior and posterior beliefs

p Prior beliefs: p Posterior beliefs:

( ) ( ; , )f beta f a bρ =

( | ) ( ; , )f d beta f a s b tρ = + +

d: 8, 2

beta(3,3): prior knowledge -- this coin may be fair

beta(11,5): posterior belief -- the coin may not be fair after

tossing 8 heads and 2 tails

53

Use Dirichlet Distribution to model prior and posterior beliefs

p Prior beliefs: p Posterior beliefs:

( ) ( ; , )f beta f a bρ =

( | ) ( ; , )f d beta f a s b tρ = + +

d: 3,0

beta(1/360,19/360): prior knowledge that an 5% chance-event may be true

beta(1/360+3,19/360): posterior belief that an 5% chance-event may be true

54

Importance of Dirichlet Distributionp In 1982, Sandy Zabell proved that, if we

make certain assumptions about an individual’s beliefs, then that individual must use the Dirichlet density function to quantify any prior beliefs about a relative frequency.

55

Some Insight on BN-based Content Clustering

Content Clustering: • Because documents and words are dependent,

➔ only close documents in the feature space can be clustered together as one topic.

fw2


in a document

fw1

fw3

⇒Incorporating human factors can possibly *link* multiple clusters together.

Bayesian Network: • Models the *practical* causal relationships..

56

Gibbs Samplingp Suppose that it is hard to sample p(x) but that it is

possible to “walk around” in X using local state transitions

p Insight: we can use a “random walk” to help us draw random samples from p(x)

p At each transition change the state of just on Xi p We can describe the transition probability as a stochastic

procedure: ■ Input: a state x1,…,xn ■ Choose i at random (using uniform probability) ■ Sample x’i from

P(Xi|x1, …, xi-1, xi+1 ,…, xn, e) ■ let x’j = xj for all j ≠ i ■ return x’1,…,x’n

57

Community Constructionp Community

■ Users with similar interests

p Formal Community Construction ■ From organization chart

p Informal Community Construction ■ Cluster people with similar evolving interests or

preferences – Content-Time-Community model

58

Goal – categorize the users into communities p Each user is a probability distribution over communities p Each community is a probability distribution over topics p Each topic is a probability distribution over words

Informal Community Construction –Content-Time-Community Model

( )ukϕ

( )kzθ

The probability of ith word accessed by a given user at tcr

The probability of the word under the jth topic

The probability of choosing the jth topic for the kth community

( ) ( ) ( ) ( )1 1

, | | , ,T C

i cr i i i j cr j crj k

P w t P w z j P z j c k t P c k t= =

= = = = =∑∑ ! !

The probability of belonging to the kth community for a user

à Incorporate the time and community into LDA model

( )jwφ

59

INPUT: ■ document-word counts

■ user-documents counts • D documents, W words, U users

■ time decay factor • tcr (current time) – t (disclose time for

each document)

Informal Community Construction –Content-Time-Community (CTC) Model

OUTPUT: ■ likely communities for a user

( ) ( ) ( )( | ) | |z

P g w P w z P z g P g∝∑: Observations

wW

D

φβ

z

T

θ αC

c

U

γU

ϕ

tcr-td

α, β, γ: hyper-parameters T: number of topics C: number of communities

60

User Modeling using Time-Sensitive Adaboostp Obtain simple classifier on each feature, e.g., setting threshold on

parameters, or binary inference on input parameters.

p The system classify whether a new document is interested by a person via Adaptive Boosting (Adaboost):

■ The final classifier is a linear weighted combination of single-feature classifiers.

■ Given the single-feature simple classifiers, assigning weights on the training samples based on whether a sample is correctly or mistakenly classified. à Boosting.

■ Classifiers are considered sequentially. The selected weights in previous considered classifiers will affect the weights to be selected in the remaining classifiers. à Adaptive.

■ According to the summed errors of each simple classifier, assign a weight to it. The final classifier is then the weighted linear combination of these simple classifiers.

p Our new Time-Sensitive Adaboost algorithm: ■ In the AdaBoost algorithm, all samples are regarded equally important at

the beginning of the learning process

■ We propose a time-adaptive AdaBoost algorithm that assigns larger weights to the latest training samples

People select apples according to their shapes, sizes, other people’s interest, etc.

Each attribute is a simple classifier used in Adaboost.

61

Community based recommendation (1)

( )final1

( )S

s ss

f sign h xα=

" #= $ %

& '∑x

The final classifier is a linear combination of the weak classifiers obtained at all iterations

- Start with a uniform distribution (“weights”) over training examples (The weights tell the weak learning algorithm which examples are important)

- Obtain a weak classifier from the weak learning algorithm, hjt:X→{-1,1}

- Increase the weights on the training examples that were misclassified

- (Repeat)

p Adaboost [Freund and Schapire 1996] ■ Constructing a “strong” learner as

a linear combination of weak learners

62

Community based recommendation (2) --Time-Sensitive AdaBoost Modeling

p In AdaBoost, the goal is to minimize the energy function:

■ All samples are regarded equally important at the beginning of the learning process

p Propose a time-adaptive AdaBoost algorithm that assigns larger weights to the latest documents to indicate their importance

p Weak learners ■ linear classifiers corresponding

to the content, community and dynamic patterns

( )1 1

expN S

i s s ii s

c h xα= =

" #−% &' (

∑ ∑

( )( ) ( )1 1

exp exp ,N S

i s i s ii s

c t t h x tα τ= =

# $− − ⋅ −' () *

∑ ∑

Algorithm: Time-Sensitive Adaboost Given: ( ) ( )1 1 1, , , , , ,N N Nx c t x c t! where ix ∈X , { }1,1ic ⊆ − , N is the size of samples in the training set; current time t, and τ For s = 1,…, S

Initialize ( ) ( )( )( )1 1 exp iD i N t tτ= ⋅ ⋅ − .

Set the weight sα of the current weak hypothesis

sh according to its weighted error rate sε

11 ln2

ss

s

εα

ε

( )−= * +

, -

where ( ) ( )1

N

s s s i ii

D i h x cε=

=∑ .

Update ( )( ) ( )( ) ( )( )

1

exp exps s i i s is

s

D i c t t h xD i

Z

α τ+

− − ⋅ −=

where sZ is a normalization term. End Find weak hypothesis by: arg min

js jh

h ε∈

=H

.

Output: the final hypothesis: ( ) ( )( )signH x F x=

where ( ) ( )1

S

s ss

F x h xα=

=∑ .

63

Experiments - Methodologyp Dataset

■ “EigyoRyoku”(Sales-Force) system (31,927 users, 26,631 documents)

■ Log files p Apr. 2004 to Mar. 2005

▪ Training data - Apr. 2004 to Feb. 2005 ▪ Test data – Mar. 2005

p Nine user actions ▪ "Login", "Register_Feedback", "Preview", "Abstract", "Document

Download", "Search", "Register", "Update", "Delete"

p Evaluation -- user satisfaction ■ How many people really downloaded the documents

among these five recommendations during the testing period

64

Formal Community RecommendationsPerformance based on formal communities

0

100

200

300

400

500

600

1 useful 2 useful 3 useful 4 useful 5 useful

No.

of p

eopl

e

Global Upper BoundCollabrative FilteringStatic CommunityCBDRCommunity Upper BoundC

BDR

CBDR

CBDR

Performance based on formal communities

0

100

200

300

400

500

600


No.

of p

eopl

e

Global Upper BoundCollabrative FilteringStatic CommunityCBDRCommunity Upper BoundC

BDR

CBDR

CBDR

90.4% of Community upper bound

65

Performance based on informal communities

0

100

200

300

400

500

600


No.

of p

eopl

e

Global Upper BoundCollabrative FilteringStatic CommunityCBDRCommunity Upper Bound

CBDR

CBDR C

BDR

Performance based on informal communities

0

100

200

300

400

500

600


No.

of p

eopl

e

Global Upper BoundCollabrative FilteringStatic CommunityCBDRCommunity Upper Bound

CBDR

CBDR C

BDR

Informal Community Recommendations

113% of Formal

community’s95.5% of

Community upper bound

66


Recommendation Systems ■ Community based Dynamic Recommendation

[Song et al. SDM’06] ■ Personalized Recommendation Driven by

Information Flow [Song et al. submitted to SIGIR’06]

p Other Related Work p Conclusions

67

In E-Commerce: Rogers’ Diffusion of Innovations Theory

0

10

20

30

40

Innovators Early adopters Early majority Late majority Laggards

16

3434

14

3

Users’ adoption patterns: Some users tend to adopt innovations earlier than others à Information virtually flows from early adopters to late adopters

68

Innovator


Early adopter

Early majorityLate majority

Laggard

Innovator


Early adopter

Early majorityLate majority

Laggard

Recommendation Driven by Information Flow

Influence is not symmetric!

adopt

adopt

?

adopt??

69

Scheme Overviewp Leverage the uneven influence

EABIF: Early Adoption based Information Flow Network

Dataset EABIF Information Propagation

Model

Application:Personalized

Recommendation

70

EABIF (1) -- Markov Chainp A Markov chain has two components

■ A network structure where each node is called a state ■ A transition probability of traversing a link given that the chain is

in a state (P: transition probability matrix) p A stationary distribution is a probability distribution q such

that q = qP à how likely you will stay at one node

p Application -- PageRank [Brin and Page ‘98] ■ Assumption: A link from page A to page B is a recommendation

of page B by the author of A à Quality of a page is related to

p Number of pages linking to it p The quality of pages linking to it

■ Assume the web is a Markov chain p PageRank = stationary distribution of this Markov chain

i jPij

C

A B

71

EABIF (2)p Early Adoption Matrix (EAB)

■ Count how many items one user accesses earlier than the other – pairwise comparison

p Markov Chain Model ■ Normalize EAB to a transition matrix F of a

Markov chain ■ Adjustment F to guarantee the existence of

stationary distribution of the Markov chain p Make the matrix stochastic p Make the Markov chain irreducible

1ijjF =∑

0, 1 ,ijF i j N≠ ≤ ≤

72

v u

r1

r2 r3…

v u

r1

r2 r3

v u

r1

r2 r3…

Information Propagation Models1. Summation of various

propagation steps

2. Direct summation

3. Exponential weighted summation

( ) ( ) ( ) ( )( )

( )2 1

(exp)1 1

exp2! 1 !

N

if Nβ β β β

−# $= + + + + ⋅ −& '& '−( )

F F F F! !

( )( )1( )

N

if d

−⋅ −=

−

F I FF

I F

( )( ) ( )( )2 m

if m m= + + +F F F F!

N: number of the nodes

73

Topic-Sensitive Early Adoption Based Information Flow (TEABIF) Network

p Adoption is typically category specific ■ An early adopter of fashion may not be an early adopter of

technology

TOPIC 1

TOPIC 3

TOPIC 2

LDA

TOPIC 1

TOPIC 3

TOPIC 2

Application: Personalized

Recommendation

74

Experimental Setupp ER dataset

■ 2004 Apr. to 2005 Apr. as training data ■ 2005 May to 2005 Jul. as test data

p 1033 users, 586 documents p Process

■ Construct information flow network based on the training data

■ Trigger earliest users to start the process ■ Predict who will be also interested in these

documents p Evaluation

■ Precision & Recall

75

Experimental Results --Recommendation Quality

Precision Comparison (Number of Triggered Users =1, Propagation Steps = 1)

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4No. of retrieved users

Prec

isio

n

CFEABIFTEABIF

Recall Comparison (Number of Triggered Users = 1,Propagation Steps = 1)

00.020.040.060.08

0.10.120.14


Rec

all

CFEABIFTEABIF

Precision Comparison (Number of Triggered Users =2, Propagation Steps = 1)

0

0.1

0.2

0.3

0.4

0.5


Prec

isio

n

CFEABIFTEABIF

Recall Comparison (Number of Triggered Users = 2,Propagation Steps = 1)

0

0.02

0.04

0.06

0.08

0.1

0.12


Rec

all

CFEABIFTEABIF

à Comparing to Collaborative Filtering (CF), precision: EABIF is 91.0% better, TEABIF is 108.5% better

Recall: EABIF is 87.1% better, TEABIF is 112.8% better

76

Experimental Results -- Propagation Performance

Precision Improvement Comparison (Number of triggeredusers = 1, Baseline: CF)

00.20.40.60.8

11.21.41.6

m =

1

m =

2

m =

3

m =

4

m =

5

sum

exp(β=

1)

exp(β=

1.5

)

exp(β=

2)

exp(β=

3)

exp(β=

4)

exp(β=

5)

exp(β=

8)

exp(β=

16)

Rat

io (

x100

%)

EABIFTEABIF

Recall Improvement Comparison (Number of triggeredusers = 1, Baseline: CF)

00.20.40.60.8

11.21.4

m =

1

m =

2

m =

3

m =

4

m =

5

sum

exp(β=

1)

exp(β=

1.5

)

exp(β=

2)

exp(β=

3)

exp(β=

4)

exp(β=

5)

exp(β=

8)

exp(β=

16)

Rat

io (

x100

%)

EABIFTEABIF

Precision Improvement Comparison (Number of triggeredusers = 2, Baseline: CF)

00.10.20.30.40.50.60.70.8

m =

1

m =

2

m =

3

m =

4

m =

5

sum

exp(β=

1)

exp(β=

1.5

)

exp(β=

2)

exp(β=

3)

exp(β=

4)

exp(β=

5)

exp(β=

8)

exp(β=

16)

Rat

io (

x100

%)

EABIFTEABIF

Recall Improvement Comparison (Number of triggered users= 2, Baseline: CF)

00.10.20.30.40.50.60.70.8

m =

1

m =

2

m =

3

m =

4

m =

5

sum

exp(β=

1)

exp(β=

1.5

)

exp(β=

2)

exp(β=

3)

exp(β=

4)

exp(β=

5)

exp(β=

8)

exp(β=

16)

Rat

io (

x100

%)

EABIFTEABIF

à TEABIF with exponential weighted summation ( ) achieves the best performance:

improves 108.5% on precision and 116.9% on recall comparing to CF

3β =

77

Summaryp Exploit dynamic patterns including

■ Leverage dynamic patterns from both documents and users’ perspective

p Analyzing documents accessing types p Predicting documents’ expiration date p Detecting users’ intentions p Identifying users interests evolving over time – CTC model p Ranking the documents adaptively – Time-sensitive Adaboost

■ Utilize users’ adoption patterns p Information virtually flows from early adopters to late adopters

p Experimental results demonstrate ■ Dynamic factors are important for recommendations

78

p X. Song, C.-Y. Lin, B. L. Tseng, and M.-T. Sun, “Modeling Evolutionary Behaviors for Community-based Dynamic Recommendation,” SIAM Conf. on Data Mining, Bethesda, MD, Apr. 2006.

p X. Song, C.-Y. Lin, B. L. Tseng, and M.-T. Sun, “Personalized Recommendation Driven by Information Flow,” ACM SIGIR, Aug. 2006.

p X. Song, C.-Y. Lin, B. L. Tseng and M.-T. Sun, “Modeling and Predicting Personal Information Dissemination Behavior,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2005.

p X. Song, B. L. Tseng, C.-Y. Lin, and M.-T. Sun, "ExpertiseNet: Relational and Evolutionary Expert Modeling," International Conference on User Modeling, Edinburgh, UK, Jul. 24-30, 2005.

Selected Publications