This document discusses personalized recommender systems for resource-based learning. It begins with an overview of folksonomy systems and models, then describes the CROKODIL application scenario which extends the folksonomy model. It reviews related work on ranking algorithms in folksonomies and recommender systems in e-learning. The research topic aims to exploit semantic information in folksonomies to rank learning resources using graph-based recommender techniques. The current progress includes a conceptual architecture and approaches using activity hierarchies and semantic tag types to generate recommendations. Future work involves analyzing ranking algorithms, implementing concepts, and evaluating the approaches.

1. Personalized Recommender Systems for
Resource-Based Learning
Ranking Learning Resources in Folksonomies
httc –
Hessian Telemedia Technology
Competence-Center e.V - www.httc.de
KOM - Multimedia Communications Lab
Prof. Dr.-Ing. Ralf Steinmetz (Director)
Dept. of Electrical Engineering and Information Technology
Dept. of Computer Science (adjunct Professor)
Dipl. –Inform. Mojisola Anjorin TUD – Technische Universität Darmstadt
Rundeturmstr. 10, D-64283 Darmstadt, Germany
Mojisola.Anjorin@KOM.tu-darmstadt.de Tel.+49 6151 166150, Fax. +49 6151 166152
Tel.+49 6151 166160 www.KOM.tu-darmstadt.de
Erster_F_Vortrag_Personalized_Rec_Sys_for_RBL__20110919_MA_v5.0.ppt 2. November 2011
© 2011 author(s) of these slides including research results from the KOM research network and TU Darmstadt. Otherwise it is specified at the respective slide

2. Resource-Based Learning
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3. Challenge: What is relevant to me right now?
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4. Solution: Ranking of Learning Resources
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5. Overview
1. Basics
2. Application Scenario: CROKODIL
3. Related Work
 Ranking Algorithms in Folksonomies
 Recommender Systems in E-learning
4. Research Topic
 Research Question
 Objectives
 Research Approach
 Current Progress
5. Future Work
6. Summary
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6. Folksonomy Systems
A folksonomy is a system of classification derived from the practice of
collaboratively creating and managing tags to annotate and categorize
content. [Peters, 2009]
a.k.a Social Tagging Systems, Collaborative Tagging Systems
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7. Folksonomy Model
A folksonomy is a quadruple
F:= (U, T, R, Y)
where
U - Users
T - Tags
R - Resources
Y ⊆ R × T × U - tag assignment
[Hotho et al, 2006]
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8. Overview
1. Basics
2. Application Scenario: CROKODIL
3. Related Work
 Ranking Algorithms in Folksonomies
 Recommender Systems in E-learning
4. Research Topic
 Research Question
 Objectives
 Research Approach
 Current Progress
5. Future Work
6. Summary
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9. Application Scenario: CROKODIL
CROKODIL is a platform offering support for resource-based learning
in professional education
 Semantic Tag Types
 Activities
 Learner Groups and Friendships
 Recommendations
[Anjorin et al, EC-TEL 2011]
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10. CROKODIL Extends the Folksonomy Model …
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11. Semantic Tag Types
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12. Learner Groups and Friendships
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13. Activities
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14. Overview
1. Basics
2. Application Scenario: CROKODIL
3. Related Work
 Ranking Algorithms in Folksonomies
 Recommender Systems in E-learning
4. Research Topic
 Research Question
 Objectives
 Research Approach
 Current Progress
5. Future Work
6. Summary
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15. Recommendation Techniques
Collaborative Filtering Recommendation Techniques:
 Nearest Neighbor (cosine, correlation)
 Clustering
 Bayesian Networks
 Neural Networks
 Probabilistic Models
 Graph-Based Techniques
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16. PageRank Algorithm (Page & Brin, 1998)
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Wikipedia Commons. An art draw drawn by Felipe Micaroni Lalli

17. Ranking Algorithms in Folksonomies
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18. Ranking Algorithms in Folksonomies
Ranking Strategy Applicable For Topic-sensitive Group-sensitive
(adapts to context)
FolkRank Users, Tags, Resources Yes No
GFolkRank Users, Tags, Resources Yes Yes
GFolkRank+ Users, Tags, Resources Yes Yes
GRank Resources Yes Yes
SocialPageRank Resources No No
Personalized Resources Yes No
SocialPageRank
[Abel et al, 2008]
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19. Recommender Systems in E-Learning
Recommender Systems Descriptions
ReMashed  Recommendations for Web 2.0 content
[Drachsler et al. 2009]  User-based collaborative filtering
 Informal Learning Networks
RACOFI Recommendations of audio Learning
(Rule-Applying Collaborative Filtering) Objects
[Anderson et al. 2003; Lemire 2005] Rule-based and Collaborative filtering
Using domain taxonomies
RPL recommender  Hybrid recommender system
[Khribi et al. 2009]  Rated recommendations
 Learning at work for specific tasks
[Manouselis et al, 2011]
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20. Overview
1. Basics
2. Application Scenario: CROKODIL
3. Related Work
 Ranking Algorithms in Folksonomies
 Recommender Systems in E-learning
4. Research Topic
 Research Question
 Objectives
 Research Approach
 Current Progress
5. Future Work
6. Summary
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21. Research Question
What semantic information in folksonomies can be exploited to rank learning
resources in graph-based recommender systems?
How can these be used to provide personalized recommendations in
resource-based learning?
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22. Objectives
1. Investigate ranking algorithms and graph-based recommender
techniques for folksonomies
2. Design and implement a personalized graph-based recommender
system for resource-based learning
1. Identify semantic information to rank learning resources in the application
scenario CROKODIL
2. Integrate relevance feedback to personalize ranking of learning resources
3. Integrate explanations for graph-based recommendations
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23. Research Approach
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24. Current Progress: Conceptual Architecture
[Anjorin et al, ISWC Workshop 2011]
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25. Current Progress: 3a & 3b
3a 3b 3c 3d
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26. Tag Weights based on Semantic Tag Types
Tag weights are determined based on the usage frequency of
semantic tag types
 Tag Types give additional information about the tag and the tag assignment
 Assuming usage frequency indicates importance of tag type
 Therefore tag types indicate the importance of tags
Tag Type Topic Person Goal Event Genre Location
Usage Frequency 30% 22% 20% 6% 5% 3%
[Böhnstedt, 2011]
[Anjorin et al, DeLFI Workshop 2011]
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27. Graph-Based Recommendations using
Semantic Tag Types
0.35 - 0.20 = 0.15
0.32 - 0.20 = 0.11
R1.1 Weight: 0.35 0.20
0.52 - 0.20 = 0.32
0.06
0.30
0.20
0.05 R1.2.1 Weight: 0.52
0.30
0.22
[Anjorin et al, DeLFI Workshop 2011]
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28. Graph-Based Recommendations using
Semantic Tag Types
Approach:
 Traverse the links between activities to find relevant resources (3-hop transitive
associations) [Huang et al, 2004]
 Weight resources based on semantic tag types
 Rank resources according to resource weights propagated along the activity
hierarchy
Aim:
 To generate recommendations for new users (alleviate the cold-start problem)
 To alleviate the data sparsity problem
[Anjorin et al, DeLFI Workshop 2011]
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29. Next Steps
Analyze Ranking Algorithms for Folksonomies
Investigate Semantic Information in the application scenario
CROKODIL
Implementation of Concepts
Evaluation of Concepts
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30. Overview
1. Basics
2. Application Scenario: CROKODIL
3. Related Work
 Ranking Algorithms in Folksonomies
 Recommender Systems in E-learning
4. Research Topic
 Research Question
 Objectives
 Research Approach
 Current Progress
5. Future Work
6. Summary
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31. Future Work: 3c & 3d
3a 3b 3c 3d
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32. Future Work: Recommendation Feedback Loop
Recommendation Feedback Loop
 Rank Resources
 Explain recommendations
 Relevance Feedback from Learner
 Re-Rank Resources
1.
Rank
2. Explain
Feedback
[Harrach and Anjorin, ITiCSE 2011]
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33. Future Work: Evaluations 5 & 6
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34. Overview
1. Basics
2. Application Scenario: CROKODIL
3. Related Work
 Ranking Algorithms in Folksonomies
 Recommender Systems in E-learning
4. Research Topic
 Research Question
 Objectives
 Research Approach
 Current Progress
5. Future Work
6. Summary
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35. Personalized Recommender Systems for
Resource-Based Learning
Motivation
 Due to the vast amount of resources 0.35 - 0.20 = 0.15
0.32 - 0.20 = 0.11
available on the Internet, learners R1.1 Weight: 0.35 0.20
require support in identifying and 0.52 - 0.20 = 0.32
0.06
ranking relevant resources for 0.30
learning purposes. 0.05
0.20
R1.2.1 Weight: 0.52
0.30
Challenge 0.22
 Exploit additional semantic Contributions
information in folksonomies to  Approach using activity hierarchies
improve graph-based and semantic tag types to rank
recommendations learning resources
 Identify semantic information in a  Conceptual architecture of a
resource-based learning scenario personalized recommender system
like CROKODIL, which could be providing explanations and
used to rank learning resources considering relevance feedback from
the learner
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36. Publications
[ARS11] Mojisola Anjorin, Christoph Rensing, Ralf Steinmetz: Towards Ranking in Folksonomies for
Personalized Recommender Systems in E-Learning (accepted for publication). October 2011.
[ARB+11] Mojisola Anjorin, Christoph Rensing, Kerstin Bischoff, Christian Bogner, Lasse Lehmann, Anna
Lenka Reger, Nils Faltin, Achim Steinacker, Andy Lüdemann, Renato Domínguez García:
CROKODIL - a Platform for Collaborative Resource-Based Learning (accepted for publication).
September 2011.
[RBP+11] Christoph Rensing, Christian Bogner, Thomas Prescher, Renato Domínguez García, Mojisola
Anjorin: Aufgabenprototypen zur Unterstützung der Selbststeuerung im Ressourcen-basierten
Lernen. DeLFI 2011, Sept 2011.
[ABR11] Mojisola Anjorin, Doreen Böhnstedt, Christoph Rensing: Towards Graph-Based
Recommendations for Resource-Based Learning using Semantic Tag Types. DeLFI 2011, Sept
2011.
[AaaC11] Mojisola Anjorin, Renato Domínguez García, Christoph Rensing: CROKODIL: a platform
supporting the collaborative management of web resources for learning purposes. ITiCSE,
ACM, June 2011.
[HA11] Sebastian Harrach, Mojisola Anjorin: Optimizing collaborative learning processes by using
recommendation systems. ITiCSE, ACM, June 2011.
[Ren11-2] Christoph Rensing,Stephan Tittel, Mojisola Anjorin: Location based Learning Content Authoring
and Content Access in the docendo platform. PerCom-WORKSHOPS 2011, March 2011.
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37. Literature
[ Abel et al, 2008] Fabian Abel, Nicola Henze, and Daniel Krause. Analyzing
Ranking Algorithms in Folksonomy Systems. Technical Report,
2008.
[Böhnstedt, 2011] Doreen Böhnstedt. Phd Thesis, Technische Universität
Darmstadt, 2011.
[Huang et al, 2004] Zan Huang, Hsinchun Chen, and Daniel Zeng. Applying
Associative Retrieval Techniques to Alleviate the Sparsity
Problem in Collaborative Filtering. ACM Transactions of
Information Systems, 2004.
[Hotho et al, 2006] Andreas Hotho, Robert Jäschke, Christoph Schmitz, and Gerd
Stumme. Information Retrieval in Folksonomies: Search and
Ranking. In ESWC, Lecture Notes in Computer Science, 2006.
[Manouselis et al, 2011] Nikos Manouselis, Hendrik Drachsler, Riina Vuorikari, Hans G.
K. Hummel, and Rob Koper. Recommender Systems in
Technology Enhanced Learning. In Recommender Systems
Handbook. Springer, 2011.
[Peters, 2010] Isabella Peters. Folksonomies. Indexing and Retrieval in Web
2.0. De Gruyter - Saur, Berlin, 2010.
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38. Any Questions ?
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