DATA-DRIVEN STUDY: AUGMENTING PREDICTION ACCURACY OF RECOMMENDATIONS IN SOCIAL LEARNING PLATFORMS
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Transcript of DATA-DRIVEN STUDY: AUGMENTING PREDICTION ACCURACY OF RECOMMENDATIONS IN SOCIAL LEARNING PLATFORMS
EU FP7 Open Discovery Space (ODS)
Research methodology 1-‐ Conceptual model 2-‐ Offline data study ✓ 3-‐ User study 4-‐ Go online
Offline data study • Classical collaboraHve
filtering based on nearest neighbors, beside a graph-‐based approach
• Three datasets similar to the ODS data plus MovieLens
DATA-‐DRIVEN STUDY: AUGMENTING PREDICTION ACCURACY OF
RECOMMENDATIONS IN SOCIAL LEARNING PLATFORMS
Degree centrality of first 10 central users
0"
50"
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250"
u1" u2" u3" u4" u5" u6" u7" u8" u9" u10"
degree%
Top)10%central%users%
MovieLens"
OpenScout"
MACE"
Travel"well"
F1 score of different user-‐based collaboraHve filtering
algorithms
SOUDE FAZELI HENDRIK DRACHSLER PETER SLOEP OPEN UNIVERSITEIT NEDERLAND [email protected]
0"0.01"0.02"0.03"0.04"0.05"0.06"0.07"0.08"0.09"0.1"
3" 5" 7" 10"
F1@10%
size%of%neighborhood%(n)%
MACE%
Tanimoto4Jaccard"(CF1)"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph4based"(CF4)"
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0.02"
0.04"
0.06"
0.08"
0.1"
0.12"
0.14"
3" 5" 7" 10"
F1@10%
size%of%neighborhood%(n)%
OpenScout%
Tanimoto3Jaccard"(CF1)"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph3based"(CF4)"
0"
0.02"
0.04"
0.06"
0.08"
0.1"
3" 5" 7" 10"
F1@10%
size%of%neighborhood%(n)%
Travel%well%
Tanimoto3Jaccard"(CF1)"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph3based"(CF4)"0"
0.05"
0.1"
0.15"
0.2"
0.25"
3" 5" 7" 10"
F1@10%
size%of%neighborhood%(n)%
MovieLens%
Tanimoto0Jaccard"(CF1)"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph0based"(CF4)"
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?