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Erster f vortrag_personalized_rec_sys_for_rbl__20110919_ma_v5.0

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Personalized Recommender Systems for Resource-Based Learning

Personalized Recommender Systems for Resource-Based Learning

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  • 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, GermanyMojisola.Anjorin@KOM.tu-darmstadt.de Tel.+49 6151 166150, Fax. +49 6151 166152Tel.+49 6151 166160 www.KOM.tu-darmstadt.deErster_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 KOM – Multimedia Communications Lab 2
  • 3. Challenge: What is relevant to me right now? KOM – Multimedia Communications Lab 3
  • 4. Solution: Ranking of Learning Resources KOM – Multimedia Communications Lab 4
  • 5. Overview1. Basics2. Application Scenario: CROKODIL3. Related Work  Ranking Algorithms in Folksonomies  Recommender Systems in E-learning4. Research Topic  Research Question  Objectives  Research Approach  Current Progress5. Future Work6. Summary KOM – Multimedia Communications Lab 5
  • 6. Folksonomy SystemsA 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 KOM – Multimedia Communications Lab 6
  • 7. Folksonomy ModelA folksonomy is a quadrupleF:= (U, T, R, Y)whereU - UsersT - TagsR - ResourcesY ⊆ R × T × U - tag assignment[Hotho et al, 2006] KOM – Multimedia Communications Lab 7
  • 8. Overview1. Basics2. Application Scenario: CROKODIL3. Related Work  Ranking Algorithms in Folksonomies  Recommender Systems in E-learning4. Research Topic  Research Question  Objectives  Research Approach  Current Progress5. Future Work6. Summary KOM – Multimedia Communications Lab 8
  • 9. Application Scenario: CROKODILCROKODIL 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] KOM – Multimedia Communications Lab 9
  • 10. CROKODIL Extends the Folksonomy Model … KOM – Multimedia Communications Lab 10
  • 11. Semantic Tag Types KOM – Multimedia Communications Lab 11
  • 12. Learner Groups and Friendships KOM – Multimedia Communications Lab 12
  • 13. Activities KOM – Multimedia Communications Lab 13
  • 14. Overview1. Basics2. Application Scenario: CROKODIL3. Related Work  Ranking Algorithms in Folksonomies  Recommender Systems in E-learning4. Research Topic  Research Question  Objectives  Research Approach  Current Progress5. Future Work6. Summary KOM – Multimedia Communications Lab 14
  • 15. Recommendation TechniquesCollaborative Filtering Recommendation Techniques:  Nearest Neighbor (cosine, correlation)  Clustering  Bayesian Networks  Neural Networks  Probabilistic Models  Graph-Based Techniques KOM – Multimedia Communications Lab 15
  • 16. PageRank Algorithm (Page & Brin, 1998) KOM – Multimedia Communications Lab 16 Wikipedia Commons. An art draw drawn by Felipe Micaroni Lalli
  • 17. Ranking Algorithms in Folksonomies KOM – Multimedia Communications Lab 17
  • 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] KOM – Multimedia Communications Lab 18
  • 19. Recommender Systems in E-LearningRecommender Systems DescriptionsReMashed  Recommendations for Web 2.0 content[Drachsler et al. 2009]  User-based collaborative filtering  Informal Learning NetworksRACOFI Recommendations of audio Learning(Rule-Applying Collaborative Filtering) Objects[Anderson et al. 2003; Lemire 2005] Rule-based and Collaborative filtering Using domain taxonomiesRPL recommender  Hybrid recommender system[Khribi et al. 2009]  Rated recommendations  Learning at work for specific tasks[Manouselis et al, 2011] KOM – Multimedia Communications Lab 19
  • 20. Overview1. Basics2. Application Scenario: CROKODIL3. Related Work  Ranking Algorithms in Folksonomies  Recommender Systems in E-learning4. Research Topic  Research Question  Objectives  Research Approach  Current Progress5. Future Work6. Summary KOM – Multimedia Communications Lab 20
  • 21. Research QuestionWhat semantic information in folksonomies can be exploited to rank learningresources in graph-based recommender systems?How can these be used to provide personalized recommendations inresource-based learning? KOM – Multimedia Communications Lab 21
  • 22. Objectives1. Investigate ranking algorithms and graph-based recommender techniques for folksonomies2. 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 KOM – Multimedia Communications Lab 22
  • 23. Research Approach KOM – Multimedia Communications Lab 23
  • 24. Current Progress: Conceptual Architecture[Anjorin et al, ISWC Workshop 2011] KOM – Multimedia Communications Lab 24
  • 25. Current Progress: 3a & 3b 3a 3b 3c 3d KOM – Multimedia Communications Lab 25
  • 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] KOM – Multimedia Communications Lab 26
  • 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 = 0.320.06 0.30 0.20 0.05 R1.2.1 Weight: 0.52 0.30 0.22[Anjorin et al, DeLFI Workshop 2011] KOM – Multimedia Communications Lab 27
  • 28. Graph-Based Recommendations usingSemantic Tag TypesApproach:  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 hierarchyAim:  To generate recommendations for new users (alleviate the cold-start problem)  To alleviate the data sparsity problem[Anjorin et al, DeLFI Workshop 2011] KOM – Multimedia Communications Lab 28
  • 29. Next StepsAnalyze Ranking Algorithms for FolksonomiesInvestigate Semantic Information in the application scenarioCROKODILImplementation of ConceptsEvaluation of Concepts KOM – Multimedia Communications Lab 29
  • 30. Overview1. Basics2. Application Scenario: CROKODIL3. Related Work  Ranking Algorithms in Folksonomies  Recommender Systems in E-learning4. Research Topic  Research Question  Objectives  Research Approach  Current Progress5. Future Work6. Summary KOM – Multimedia Communications Lab 30
  • 31. Future Work: 3c & 3d 3a 3b 3c 3d KOM – Multimedia Communications Lab 31
  • 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] KOM – Multimedia Communications Lab 32
  • 33. Future Work: Evaluations 5 & 6 KOM – Multimedia Communications Lab 33
  • 34. Overview1. Basics2. Application Scenario: CROKODIL3. Related Work  Ranking Algorithms in Folksonomies  Recommender Systems in E-learning4. Research Topic  Research Question  Objectives  Research Approach  Current Progress5. Future Work6. Summary KOM – Multimedia Communications Lab 34
  • 35. Personalized Recommender Systems forResource-Based LearningMotivation  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 0.30Challenge 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 KOM – Multimedia Communications Lab 35
  • 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. KOM – Multimedia Communications Lab 36
  • 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. KOM – Multimedia Communications Lab 37
  • 38. Any Questions ? KOM – Multimedia Communications Lab 38