The document discusses research into recommender systems for social learning platforms. It evaluates content-based, collaborative filtering, memory-based, model-based, and graph-based recommender approaches using datasets from MACE, OpenScout, and MovieLens. Graph-based recommender systems performed best by addressing the sparsity problem in educational domains. The implemented graph-based recommender has been integrated into the Open Discovery Space platform and will undergo a user study to evaluate satisfaction metrics like novelty and serendipity.

1. Which Recommender System Can Best
Fit Social Learning Platforms?
Soude Fazeli, PhD candidate, OUNL
Babak Loni, TU Delft
Dr. Hendrik Drachsler, OUNL
Prof. dr. Peter Sloep, OUNL

2. Which Recommender System Can
Best Fit Social Learning
Platforms?
Open Discovery Space (ODS)
A socially-powered, multilingual
open learning infrastructure
in Europe
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Recommendations!
Which recommender approach best fits ODS platform?

3. Recommender algorithms
1. Content-based 2. Collaborative filtering
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✓

4. Pagina 4
Sparsity!
Similarity

5. Learning domain has its own data, limitations, and
expectations
• Too sparse data
• Too few 5-star ratings
• Often no proper tags and annotations
• Can not use only popular reference datasets like MovieLens, Netflix, etc.
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Dataset
Users
Learning
objects
Transactions
Sparsity
(%)
MACE 105 5,696 23,032 99.71
OpenScout 331 1,568 2,560 99.51
MovieLens
941 1,512 96,719 93.69
100k

6. RQ: How to generate more accurate and thus
more relevant recommendations for the users in
social learning platforms by employing graph-based
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methods?

7. A graph-based recommender system
• Implicit networks: a graph
– Nodes: users
– Edges: similarity relationships
– Weights: similarity values
• Improve the process of finding nearest
neighbors
– Social Index (S-index) for each user
– H-index: the impact of publications of an
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author

8. Creating the graph
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Algorithm 1 Computing S-index for user u
upon event (COMPUTE S-INDEX| u, NeighborsList)
SortedNeighborsList SortDescendingBySimilarityScore(NeighborsList);
FinalNeighborsList Normalize(SortedNeighborsList,MaximumSindex);
Sindex 0;
for ( similarityScore(u,n); n in FinalNeighborsList) do
if Sindex <= similairtyScore then
Sindex= Sindex+1;
else
Break;
end if
end for
updateSindex(Sindex);
end event

9. Indegree (ua) = 7
Indegree (ub) = 5
S-index (ua ) = 2
S-index (ub ) = 4
Onderwerp via >Beeld >Koptekst en voettekst
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10. Collecting recommendations
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G(V,E) = CreateSocialGraph(); // V contains users
// E contains similarity relations between users
for all uV do
ComputeSindex(u, N); // N contains users who have user u as their neighbor
G(V,E′) BFS(u, G(V,E)); // E E′ where E′ contains:
// 1. explicit similarity relations (u,n) E and
// 2. new inferred relations (u, n′)
TopItems CollectRecommendations(u, G(V, E′));
UpdateSindex(u,N’); // N’ contains new neighbors found
UpdateSocialGraph();
end for

11. Experimental study
1. The method
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First$step$ Third$step$
Memory'based,
CFs,
Model'based,CFs,
User'based,
Item'based,
Jaccard,kNN,
Loglikelihood,
kNN,
Euclidean,kNN,
MostPopular,
BPRMF,,
BPRSLIM,
Best'performing,
memory'based,
CF,
Graph'based,CF,
Best'performing,
model'based,CF,
UB1$
UB2$
UB3$
IB1$
IB2$
IB3$
Final$comparison$
Second$step$

12. Experimental study
2. Data
1. The datasets contain social data of users such as ratings, tags, reviews, etc. on
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learning resources. So, their structure, content and target users are quite similar
to the ODS dataset.
2. Running recommender algorithms on these datasets enables us to conduct an
offline experiment for studying the recommender algorithms before going online
with the actual users of the ODS.
3. Both MACE and OpenScout datasets comply with the CAM (Context Automated
Metadata) format. CAM is also applied in the ODS project for storing the social
data.
Dataset Users Learning
objects
Transactions
Sparsity
(%)
MACE 105 5,696 23,032 99.71
OpenScout 331 1,568 2,560 99.51
MovieLens 941 1,512 96,719 93.69

13. Experimental study
3. Results
3.1. Memory-based CFs
X-axis: Size of neighbors
Y-axis: F1@10
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OpenScout MACE MovieLens

14. Experimental study
3. Results
3.2. Model-based CFs
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
MACE OpenScout MovieLens
F1@10%
Datasets%
BPRMF
BPRSLIM
MostPopular
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15. Experimental study
3. Results
3.3. Final step
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0.3
0.25
0.2
10%
F1@0.15
0.1
0.05
0
MACE OpenScout MovieLens Datasets%
Memorybased
Modelbased
Graphbased

16. Conclusion
• The aim is to support users of social learning
platforms in finding relevant resources
• Graph-based recommender systems can help to
deal with sparsity problem in educational domain
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17. Ongoing and Further work
• The graph-based recommender already has been integrated with
ODS platform
• Using trust relationships
– Explicit vs implicit trust relations (accepted paper at
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#RecSys2015 in Silicon Valley, US, Oct. 2014)
• User study (November- December 2014) to evaluate user
satisfaction
– Novelty, serendipity, diversity: very important for learning
domain

18. Many thanks to the FIT Fraunhofer Institute, especially Dr. Martin
Wolpers and Katja Niemann, for providing us with both the MACE
and OpenScout datasets on a short notice. Without their support,
this study would not have been possible.
Onderwerp via Beeld Koptekst en voettekst
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19. soude.fazeli[at]ou[dot]nl
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@SoudeFazeli