=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards Ranking in Folksonomies for Personalized Recommender Systems in E-Learning
|pdfUrl=https://ceur-ws.org/Vol-781/paper3.pdf
|volume=Vol-781
|dblpUrl=https://dblp.org/rec/conf/semweb/AnjorinRS11
}}
==Towards Ranking in Folksonomies for Personalized Recommender Systems in E-Learning==
https://ceur-ws.org/Vol-781/paper3.pdf
Towards Ranking in Folksonomies for
Personalized Recommender Systems in
E-Learning
Mojisola Anjorin, Christoph Rensing, and Ralf Steinmetz
Technische Universität Darmstadt,
Multimedia Communications Lab,
64283 Darmstadt, Germany
{Mojisola.Anjorin, Christoph.Rensing, Ralf.Steinmetz}@kom.tu-darmstadt.de
Abstract. Recommender systems offer the opportunity for users to no
longer have to search for resources but rather have these resources of-
fered to them considering their personal needs and contexts. Additional
semantics found in a folksonomy can be exploited to enhance the rank-
ing of resources. These semantics have been analyzed in an e-learning
scenario: CROKODIL. CROKODIL is a platform which supports the
collaborative acquisition and management of learning resources. This
paper proposes a conceptual architecture describing how these seman-
tics can be integrated in a personalized recommender system for learning
purposes.
1 Introduction
Recent research on personalized recommender systems has shown that the ex-
ploitation of semantic information found in folksonomy systems have led to
improved recommendations [1]. Recommender systems have been applied to e-
learning scenarios to help provide personalized support to learners by suggesting
relevant items for learning purposes [6]. This raises the challenge of identifying
relevant resources which match the current personal context and needs of the
learners. Recommender systems in learning scenarios pose new requirements such
as exploring and identifying which attributes represent relevance in a learning
context [6]. It is therefore an ongoing challenge to meet the requirements of rec-
ommender systems in an e-learning scenario such as CROKODIL1 . CROKODIL
is based on a pedagogical concept which focuses on activities as the central struc-
ture to organize learning resources. The platform offers collaborative semantic
tagging (thereby creating a folksonomy) as well as social network functionality
to support the learning community [3].
Section 3 gives a brief analysis of the semantic information which could be ex-
ploited for the context-specific ranking of learning resources in the CROKODIL
e-learning scenario. Section 4 describes a preliminary conceptual architecture of
a personalized recommender system considering context-specific ranking. This
concept will be implemented and evaluated in future work.
1
http://www.crokodil.de, http://demo.crokodil.de (online as of 12.09.2011)
22
2 Related Work
A survey of the state-of-the-art on social tagging systems and how they ex-
tend the capabilities of recommender systems is given in [7]. Abel [1] shows
that it is worth exploiting additional context information which are found in
folksonomies to improve ranking strategies. Approaches are introduced which
extend FolkRank to a context-sensitive ranking algorithm exploiting the addi-
tional semantics relating to groups of resources in GroupMe! [1]. In e-learning,
recommender systems exist using context variables such as user attributes and
domain specific information to provide personalized recommendations [6]. The
concept in this paper proposes to use contextual information in folksonomies to
rank learning resources in a personalized recommender system for e-learning.
3 Context Feature Analysis in an E-Learning Scenario
Contextual information in folksonomies can be categorized into four dimensions
[1]: the user context, the tag context, the resource context and the tag assignment
context (when a user attaches a tag to a resource). Considering the e-learning
scenario CROKODIL, the available contextual information can be categorized as
shown in Table 1. The user context comprises of: learner groups working together
on a common task or activity, user roles such as the tutor role and friendships
existing between individual learners. In future work, additional social informa-
tion could be inferred from the learner’s social network. When tagging resources
in CROKODIL, tag types such as genre, topic, location, person and event can
be assigned to the tags (a tag without a type is also possible). For example, the
tag “Beer” of type “Location” refers to the town in Devon, England and not
to the alcoholic beverage “beer”, thus providing contextual information to the
individual tags as well as to the tag assignments. Activities provide contextual
information to a resource. In CROKODIL, activities structure which tasks need
to be performed to achieve a defined goal. For example, in order to get ready to
hold a presentation on German Culture, a learner creates an activity called “Pre-
pare a talk about the Oktoberfest in Germany” having a sub-activity “Describe
popular brands of beer in Bavaria”. The required knowledge for this presentation
Table 1. Context Dimensions Applied to the CROKODIL Scenario
User Context Tag Context Resource Context Tag Assignment Context
learner groups, tag types activities tag types,
user roles, friendships activities
is sought mostly via resources on the Web, such as on blogs or in Wikipedia.
The appropriate resources are then attached to the activity. This activity thus
provides contextual information to the resources. The tag assignment context
comprises of tag types and activities as these both give additional contextual
meaning when tagging.
23
4 Conceptual Architecture of a Personalized
Recommender System for E-Learning
Fig.1 shows the design of a conceptual architecture of a personalized recom-
mender system considering the CROKODIL e-learning scenario. This concept
incorporates the ranking of learning resources considering the context features
discussed in Section 3. Resources from friends, group members or the tutor
could be given a higher weight than other resources. The tag types will have
different weights according to the popularity of the tag type [2]. For example, a
topic tag “Oktoberfest” is weighted higher than a genre tag “blog”. Considering
the activity the learner is presently working on, for example, “Prepare a talk
about the Oktoberfest in Germany”, resources belonging to activities nearer in
the hierarchy will be weighted higher than resources belonging to an activity
further away [2]. Therefore, resources belonging to its direct sub-activity “De-
scribe popular brands of beer in Bavaria” are weighted higher than resources
belonging to other activities further down the hierarchy. The results from the
CROKODIL
CROKODIL
Social
External Folksonomy
Network
Resources
Activity Learner Groups,
Tag Types Hierarchy User Roles,
Friendships
Folksonomy-based
Recommender
System
Recommendation
& Explanation
Relevance
Explanations
Ranking
Relevance
Feedback
Fig. 1. Conceptual Architecture of a Personalized Recommender System
folksonomy-based recommnender system will be offered to the learner in the
form of a ranked list. The learner is given the opportunity to give explicit feed-
back regarding these recommendations via a simple like/ dislike binary rating.
This feedback is integrated into the ranking algorithm by applying Rochio’s rel-
evance feedback approach [5]. The feedback is thus used to further adapt and
fit the recommendations to the learner’s present learning context. In addition,
explanations will be made about the recommended item, giving reasons why this
item was recommended [4]. This will help to give a better understanding and
stimulate the learner to reflect about the recommended learning resources. The
24
learner is then able to give qualified feedback about whether this recommen-
dation is appropriate to the current learning needs or not. Finally, in order to
enrich the variety of resources suggested by the recommender system, external
learning resources from existing learning repositories such as ARIADNE [8] or
the Open University’s OpenLearn 2 will be considered.
5 Conclusion
In this paper, a conceptual design of a personalized recommender system for
e-learning is described applying context-specific ranking of resources. An ap-
proach for a graph-based recommender system using semantic tag types has
already been proposed in [2]. Next steps will be to implement these concepts
and integrate them in CROKODIL. The impact of the various semantic infor-
mation sources will be evaluated by considering several variants of the ranking
algorithm, thereby showing which context features or combinations thereof are
suitable to the CROKODIL learning scenario. Furthermore, investigations will
need to be made on how explanations can be generated. In addition, the accep-
tance of these explanations, relevance feedback and recommendations of external
learning resources will be evaluated with learners in a usability study.
Acknowledgements This work is supported by funds from the German
Federal Ministry of Education and Research and the mark 01 PF 08015 A and
from the European Social Fund of the European Union (ESF). The responsibility
for the contents of this publication lies with the authors.
References
1. Abel, F.: Contextualization, User Modeling and Personalization in the Social Web.
Phd thesis, Gottfried Wilhelm Leibniz University Hannover (2011)
2. Anjorin, M., Böhnstedt, D., Rensing, C.: Towards graph-based recommendations
for resource-based learning using semantic tag types. DeLFI (Sept 2011)
3. Anjorin, M., Rensing, C., Bischoff, K., Bogner, C., Lehmann, L., Reger, A.L., Faltin,
N., Steinacker, A., Lüdemann, A., Garcia, R.D.: Crokodil - a platform for collabo-
rative resource-based learning. To be published: EC-TEL (Sept 2011)
4. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems An
Introduction. CUP (2011)
5. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval:
Relevance-Feedback und Query-Expansion. CUP (2009)
6. Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H.G.K., Koper, R.: Recom-
mender systems in technology enhanced learning. In: Recommender Systems Hand-
book. Springer (2011)
7. Milicevic, A., Nanopoulos, A., Ivanovic, M.: Social tagging in recommender systems:
a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review
33, 187–209 (2010)
8. Neven, F., Duval, E.: Reusable learning objects: a survey of lom-based repositories.
Proceedings of the tenth ACM int. conf. on Multimedia (2002)
2
http://openlearn.open.ac.uk (online as of 12.09.2011)
25