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Exploiting Semantic Information for Graph-based Recommendations of Learning Resources
Mojisola Anjorin Thomas Rodenhausen Renato Domínguez García Christoph Rensing
EC-TEL 2012, Saarbrücken
Research Talk
Ranking
Algorithms
Slideshare
Tags ResourcesUsers
Prepare Talk
Read-Up on Basics
Activities
Find Related Work
Friends
Friends
FriendsBlue Group
KOM – Multimedia Communications Lab 2
Resource-Based Learning
KOM – Multimedia Communications Lab 3
Application Scenario: CROKODIL
CROKODIL is a platform offering support for resource-based learning § Semantic Tag Types § Activities § Learner Groups & Friendships § Recommendations
[Anjorin et al, 2011]
http://demo.crokodil.de
KOM – Multimedia Communications Lab 4
§ Motivation: Resource-based Learning § Application Scenario: CROKODIL § CROKODIL’s Extended Folksonomy Model § Ascore and AInheritScore § Evaluation Methodology, Metrics and Results § Conclusion & Future Work
Overview
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A folksonomy is a quadruple F:= (U, T, R, Y), where U – Users T – Tags R – Resources Y ⊆ U × T × R - tag assignment
Folksonomy Model
Research Talk
Ranking
Algorithms
Slideshare
Tags ResourcesUsers
[Hotho et al. 2006]
KOM – Multimedia Communications Lab 6
CROKODIL Extends the Folksonomy Model …
Research Talk
Ranking
Algorithms
Slideshare
Tags ResourcesUsers
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… with Semantic Tag Types
[Böhnstedt et al. 2009]
Research Talk
Ranking
Algorithms
Slideshare
Tags ResourcesUsers
Genre
Event
Person
Location
Other
Topic
KOM – Multimedia Communications Lab 8
… with Activities
Research Talk
Ranking
Algorithms
Slideshare
Tags ResourcesUsers
Prepare Talk
Read-Up on Basics
Activities
Find Related Work
KOM – Multimedia Communications Lab 9
… with Learner Groups and Friendships
Research Talk
Ranking
Algorithms
Slideshare
Tags ResourcesUsers
Prepare Talk
Read-Up on Basics
Activities
Find Related Work
Friends
Friends
FriendsBlue Group
KOM – Multimedia Communications Lab 10
CROKODIL‘s Extended Folksonomy
FC:= (U, TTyped, R, YT, (A, <), YA, YU, G, friends) where U – users TTyped – typed tags R – learning resources YT ⊆ U × TTyped × R – tag assignment (A, <) – activities with sub-activities YA ⊆ U × A × R – activity assignment YU ⊆ U × A – activity membership
assignment G ⊆ P(U) – groups of learners friends ⊆ U × U – friendship relation
Research Talk
Ranking
Algorithms
Slideshare
Tags ResourcesUsers
Prepare Talk
Read-Up on Basics
Activities
Find Related Work
Friends
Friends
FriendsBlue Group
KOM – Multimedia Communications Lab 11
Resource Recommendations for CROKODIL
http://demo.crokodil.de
KOM – Multimedia Communications Lab 12
Graph-based recommender techniques can be classified as neighbourhood-based collaborative filtering approaches
Graph-based Resource Recommendations
Graph-based Ranking
Algorithm
Resource Score r1 0.9 r2 0.7 r3 0.5 r4 0.2
1 1
2 1
P1
P2
P4
P3
3
4
2
1
2
Folksonomy Graph e.g. FolkRank based on “Random Walk” of PageRank
Recommendation List (ranked resources)
[Desrosiers et al. 2011]
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§ Motivation: Resource-based Learning § Application Scenario: CROKODIL § CROKODIL’s Extended Folksonomy Model § Ascore and AInheritScore § Evaluation Methodology, Metrics and Results § Conclusion & Future Work
Overview
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1. Add activity nodes Vc = VF ∪ A 2. Add edges: § activity assignments (u, r, a) § assignments of a user to an
activity (u, a) § activity hierarchies (asub , asuper)
Exploiting hierarchical activity structures as found in CROKODIL can improve the ranking of resources for the purpose of recommending learning resources § AScore § AInheritscore
Future Work § Evaluation using a data set from CROKODIL § User Study § Hybrid approaches
Conclusion and Future Work
www.crokodil.de
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Questions & Contact
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Statistical Significance Tests – LeavePostOut
More effective than à
Popularity Folk Rank
GFolkRank
AScore GRank AInheritScore
Poularity FolkRank X GFolkRank X X X X X AScore X X X X GRank X X AInheritScore X X X
Significance matrix of pair-wise comparisons of LeavePostOut results Based on Average Precision with a significance level of p = 0.05
KOM – Multimedia Communications Lab 34
Statistical Significance Tests – LeaveRTOut
More effective than à
Popularity Folk Rank
GFolkRank
AScore GRank AInheritScore
Poularity FolkRank X X X GFolkRank X X X X AScore X X X X X GRank X X AInheritScore X
Significance matrix of pair-wise comparisons of LeaveRTOut results Based on Average Precision with a significance level of p = 0.05
KOM – Multimedia Communications Lab 35
Adapted PageRank
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PageRank‘s intelligent surfer model The ranking of a node is determined by how often the surfer visits the node Adjoining edges are followed with a certain probability – determined by the edge weights The query node acts as the starting point and focus i.e. the surfer returns to this node with a certain probability – determined by the node weights