enkh-amgalan baatarjav jedsada chartree thiraphat meesumrarn university of north texas
TRANSCRIPT
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Group Recommendation System for Facebook
Enkh-Amgalan BaatarjavJedsada ChartreeThiraphat Meesumrarn
University of North Texas
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Overview
Evolution of Communication
Online Social Networking (OSN)
Architecture Profile feature Profile Analysis Similarity inference Clustering coefficient Decision tree
Conclusion
Traditional medium of communication Mail, telephone, fax,
E-mail, etc. Key to successful
communication Sharing common
value
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Online Social Networking
User-driven content Overwhelming number of groups Finding suitable groups Sharing a common value Improving online social network
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Architecture
Profile feature extraction
Classification engine Clustering Building decision
tree Group
recommendation
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Profile Feature
Group profile defined by profile features of users Time Zone - Age Gender - Relationship Status Political View - Activities Interest - Music TV shows - Movies Books - Affiliations Note counts - Wall counts Number of Fiends
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Profile AnalysisSubtype Size Description
G1 Friends 12 Friends group for one is going abroad
G2 Politic 169 Campaign for running student body
G3 Languages 10 Spanish learners
G4 Beliefs & causes 46 Campaign for homecoming king and queen
G5 Beauty 12 Wearing same pants everyday
G6 Beliefs & causes 41 Friends group
G7 Food & Drink 57 Lovers of Asian food restaurant
G8 Religion/Spirituality 42 Learning about God
G9 Age 22 Friends group
G10 Activities 40 People who play clarinets
G11 Sexuality 319 Against gay marriage
G12 Beliefs & causes 86 Friends group
G13 Sexuality 36 People who thinks fishnet is fetish
G14 Activities 179 People who dislike early morning classes
G15 Politics 195 Group for democrats
G16 Hobbies & Crafts 33 People who enjoys Half-Life (PC game)
G17 Politics 281 Not a Bush fan
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G170%
20%
40%
60%
80%
Hidden 15-19 20-24 25-29 30-36
Perc
enta
ge o
f M
em
bers
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G170%
20%
40%
60%
80%
100%
Male Female
Perc
enta
ge o
f M
em
bers
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G17
0%
20%
40%
60%
Hidden VL Li M C VC A Ln
Groups
Perc
enta
ge o
f M
em
bers
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Similarity Inference
Hierarchical clustering Normalizing data [0,
1] Computing distance
matrix to calculate similarity among all pairs of members (a)
Finding average distance between all pairs in given two clusters s and r
N
isrrs xxd
1
2)(
r sn
i
n
jsjri
sr
xxdistnn
srd1 1
),(1
),(
(a)
(b)
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Clustering Coefficient
- Ri is the normalized Euclidean distance from the center of member i
- Nk is the normalized number of members within distance k from the center
i
R
R
NC i
jj
ii r
rR
maxarg
M
nN kk
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
1.2
Ri
C
RX
Cmax
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Decision Tree
Decision tree algorithm, based on binary recursive partitioning
Splitting rules Gini, Twoing, Deviance
Tree optimization Cross-validation (computation intense)
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After Data Cleaning
Fair representation of group profile Groups must have at least 10
members Reduction
Users from 1,580 to 1,023 Group from 17 to 7
Group Size
1 274
2 226
3 159
4 151
5 133
6 67
7 13
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Result 1
Data set Training: 75% Testing: 25%
Accuracy calculation 25 fold test
Accuracy 27%
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Statistical Analysis: Mean
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Statistical Analysis: STD
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Adjustment in Feature Selection
Feature score calculation Using group profile: FSGP
Using group closeness: FSGC
Combination of FSGP and FSGC: FSPC
)( gff GPSTDFSGP
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FSGP vs Accuracy
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FSGC vs Accuracy
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FSPC vs Accuracy
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Result 2
Feature Score Calculation Accuracy (%)
Group–Profile Feature 24.47
STD of means 25.04
Mean of STDs 21.75
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Conclusion
Improving QoS of Online Social Networking Architecture
Hierarchical clustering Threshold value to reduce noise Decision tree
Result poor performance cause Decision tree: decision boundaries || to coord. Data overlapping More work on data cleaning
Feature reduction From 12 to 2