group recommendation system for facebook
DESCRIPTION
E nkh-Amgalan Baatarjav Jedsada Chartree Thiraphat Meesumrarn. Group Recommendation System for Facebook. University of North Texas. Overview. Evolution of Communication Online Social Networking (OSN) Architecture Profile feature Profile Analysis Similarity inference - PowerPoint PPT PresentationTRANSCRIPT
Group Recommendation System for Facebook
Enkh-Amgalan BaatarjavJedsada ChartreeThiraphat Meesumrarn
University of North Texas
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
Online Social Networking User-driven content Overwhelming number of groups Finding suitable groups Sharing a common value Improving online social network
Architecture
Profile feature extraction
Classification engine Clustering Building decision
tree Group
recommendation
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
Profile AnalysisSubtype Size Description
G1 Friends 12 Friends group for one is going abroadG2 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
embe
rs
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
G11
G12
G13
G14
G15
G16
G170%
20%40%60%80%
100%
Male FemalePe
rcen
tage
of
Mem
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 LnGroups
Perc
enta
ge o
f M
embe
rs
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)
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
RN
C i
jj
ii r
rRmaxarg
MnN k
k
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
Decision Tree
Decision tree algorithm, based on binary recursive partitioning
Splitting rules Gini, Twoing, Deviance
Tree optimization Cross-validation (computation intense)
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
Result 1
Data set Training: 75% Testing: 25%
Accuracy calculation 25 fold test
Accuracy 27%
Statistical Analysis: Mean
Statistical Analysis: STD
Adjustment in Feature Selection Feature score calculation
Using group profile: FSGP
Using group closeness: FSGC
Combination of FSGP and FSGC: FSPC
)( gff GPSTDFSGP
FSGP vs Accuracy
FSGC vs Accuracy
FSPC vs Accuracy
Result 2
Feature Score Calculation Accuracy (%)
Group–Profile Feature 24.47
STD of means 25.04
Mean of STDs 21.75
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