Poster presented at the 25th Benelux Conference on Artificial Intelligence (BNAIC 2013), TUDelft, the Netherlands

1. 250"
200"
150"
MovieLens"
degree%
OpenScout"
100"
MACE"
Travel"well"
50"
0"
u1"
u2"
u3"
u4"
u5"
u6"
u7"
u8"
u9"
u10"
Top)10%central%users%
Degree
centrality
of
first
10
central
users
EU
FP7
Open
Discovery
Space
(ODS)
DATA-­‐DRIVEN
STUDY:
AUGMENTING
PREDICTION
ACCURACY
OF
RECOMMENDATIONS
IN
SOCIAL
LEARNING
PLATFORMS
MACE%
0.1"
Tanimoto4Jaccard"(CF1)"
Loglikelihood"(CF2)"
0.08"
Tanimoto3Jaccard"(CF1)"
0.06"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
0.04"
Graph4based"(CF4)"
0.02"
Euclidean"(CF3)"
Graph3based"(CF4)"
0"
5"
7"
3"
10"
5"
0.2"
0.06"
Tanimoto3Jaccard"(CF1)"
0.04"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph3based"(CF4)"
3"
5"
7"
10"
size%of%neighborhood%(n)%
•
Tanimoto0Jaccard"(CF1)"
0.15"
Loglikelihood"(CF2)"
0.1"
Euclidean"(CF3)"
0.05"
Graph0based"(CF4)"
0"
0"
Classical
collaboraHve
filtering
based
on
nearest
neighbors,
beside
a
graph-­‐
based
approach
Three
datasets
similar
to
the
ODS
data
plus
MovieLens
F1@10%
F1@10%
0.25"
0.02"
•
10"
MovieLens%
Travel%well%
0.1"
0.08"
Offline
data
study
7"
size%of%neighborhood%(n)%
size%of%neighborhood%(n)%
1-­‐
Conceptual
model
2-­‐
Offline
data
study
✓
3-­‐
User
study
4-­‐
Go
online
OPEN
UNIVERSITEIT
NEDERLAND
FIRSTNAME.LASTNAME@OU.NL
0.12"
3"
Research
methodology
SOUDE
FAZELI
HENDRIK
DRACHSLER
PETER
SLOEP
OpenScout%
0.14"
F1@10%
F1@10%
RQ1:
How
to
generate
more
accurate
recommendaHons
by
taking
into
account
user
interacHons
in
social
learning
pla_orms?
RQ2:
Can
the
use
of
the
graph
walking
algorithms
improve
the
process
of
finding
like-­‐
minded
users
within
a
social
learning
pla_orm?
0.1"
0.09"
0.08"
0.07"
0.06"
0.05"
0.04"
0.03"
0.02"
0.01"
0"
3"
5"
7"
10"
size%of%neighborhood%(n)%
F1
score
of
different
user-­‐
based
collaboraHve
filtering
algorithms