=Paper=
{{Paper
|id=None
|storemode=property
|title=A Framework for Cross-Platform Graph-based Recommendations for TEL
|pdfUrl=https://ceur-ws.org/Vol-896/paper7.pdf
|volume=Vol-896
|dblpUrl=https://dblp.org/rec/conf/ectel/AnjorinDFR12
}}
==A Framework for Cross-Platform Graph-based Recommendations for TEL==
https://ceur-ws.org/Vol-896/paper7.pdf
A Framework for Cross-Platform Graph-based
Recommendations for TEL
Mojisola Anjorin1 , Ivan Dackiewicz2 , Alejandro Fernández2 , and Christoph
Rensing1
1
Multimedia Communications Lab,
Technische Universität Darmstadt, Germany
{mojisola.anjorin,christoph.rensing}@kom.tu-darmstadt.de
2
LIFIA-UNLP, La Plata, Argentina
{idackiewicz,alejandro.fernandez}@lifia.info.unlp.edu.ar
Abstract. A Technology Enhanced Learning (TEL) ecosystem is a kind
of Digital Ecosystem formed by independent platforms combined and
used by learners to support their learning. We believe that recommenda-
tions made across these different platforms by exploiting the synergies be-
tween them will benefit learners. However, building such cross-platform
recommender systems poses new and unique challenges for developers.
In this paper, we present a framework to support the development of
cross-platform recommender systems for TEL ecosystems and discuss
challenges faced. The framework decouples the development of the rec-
ommender system from the evolution of the specific platforms by combin-
ing graph-based algorithms, a unified data model, and a service oriented
architecture. As proof of concept, the framework was effectively applied
to develop a cross-platform recommender system in a TEL ecosystem
having Moodle as the Learning Management System, Mahara as the So-
cial Networking Service and Ariadne as Learning Object Repository.
Keywords: TEL, Recommender Systems, Cross-Platform, Framework
1 Introduction
A Technology Enhanced Learning (TEL) ecosystem, is a form of a Digital Ecosys-
tem [2] inhabited by elements from various platforms used in parallel by learners
and teachers. Such a simultaneous use of platforms is often found in commu-
nities of practice [9] also known as learning networks, where learning is mostly
self-directed. In this paper, we focus on a TEL ecosystem with three platforms:
a Learning Management Systems (LMS), a Social Networking Service (SNS),
and a Learning Object Repository (LOR). An LMS offers activities as well as
discussion forums and shared spaces such as wikis. Activities rely on learning
objects (LOs) such as lesson notes and presentations. The visibility of a LO is
normally limited to an activity. However when an LMS is used to support self-
directed learning, it becomes particularly important that learners are aware of
all activities, resources and peers they could potentially gain from. Nowadays,
RecSysTEL 2012 83
M. Anjorin, I. Dackiewicz, A. Fernández and C. Rensing
many learners participate in social networks connecting to other learners via
Facebook3 , or posting learning tasks and following other learners on Twitter4 .
Contacts the students have on platforms such as an LMS are disconnected from
the online social networks they belong to outside the classroom. It is therefore
up to the students to replicate in each of these worlds the relationships they
have built in the other. The potential to share knowledge and find valuable con-
tacts across these platforms therefore remains unexploited. Initiatives such as the
MIT OpenCourseWare5 or the Ariadne Foundation6 with its LOR demonstrate
the increasing interest in collecting and sharing high quality learning material.
LORs however are isolated from the LMS and SNS. There therefore exists an
opportunity to provide learners with information across multiple platforms by
considering the synergies between them.
In the following sections we propose a framework to empower a TEL ecosys-
tem by generating cross-platform recommendations in each of them based on
resources gained from all of them.
2 Related Work
Recommender systems based on approaches such as content based and collabo-
rative filtering (CF) techniques have been shown to be very useful in TEL scenar-
ios, especially in informal learning [8]. CF approaches use community data such
as feedback or ratings from other users to make recommendations. Graph-based
recommender techniques can be classified as neighborhood-based CF approaches
[4]. A graph is used to represent the users or items as nodes and the edges as
the transactions between them. PageRank [3] is an example of a graph-based
approach based on a random walk similarity. Transitive associations are defined
within a probabilistic framework where the similarity or affinity between nodes
is calculated as a probability of reaching these nodes in a random walk on a
weighted graph having a node for each state. The probability of jumping from
one node to another is given by the weight of the edge connecting these nodes.
In this paper, we implement PageRank using the information from the platforms
that make up the ecosystem, to generate recommendations across them.
ReMashed [5] is a Mash-up Personal Learning Environment allowing learners
to combine content from different Web 2.0 services to a personal view or mash-up.
Learning resources are recommended using a CF approach that matches users
with similar opinions and considers the learning goals of the learner. In contrast,
we propose a framework to recommend activities, users and LOs across multiple
platforms, thus pointing the learners to other valuable sources of information
found on these different platforms without building a mash-up.
Recommender systems are often implemented as closed, internal components
of larger applications having tightly coupled components. In contrast, APOS-
3
http://facebook.com (last retrieved 30.06.2012)
4
http://twitter.com (last retrieved 10.07.2012)
5
http://ocw.mit.edu/index.htm (last retrieved 10.07.2012)
6
http://www.ariadne-eu.org/ (last retrieved 10.07.2012)
RecSysTEL 2012 84
A Framework for Cross-Platform Graph-based Recommendations for TEL
DLE [1] for example, follows the SOA approach providing web services to publish
knowledgeable person recommendations. Web services decouple the generation of
recommendations from its presentation to the users. Our framework uses a sim-
ilar approach. Furthermore, graph-based approaches are suitable for integrating
data from various platforms, using the graph as the grounds for inter-operation.
This is particularly interesting when combined with vocabularies and technolo-
gies that originate in the Semantic Web and Linked Open Data movements [6].
3 Cross-Platform Recommendation Framework
The framework is shown in Fig.1 where the TEL ecosystem comprises of an
LMS, an SNS and a LOR. These platforms are independent of each other and
have been implemented autonomously. The introduction of recommendations
should neither increase coupling between these platforms, nor require intrusive
changes that will hinder their maintenance. Moreover, the choice of platforms
to be integrated must remain flexible, allowing for new alternatives to be intro-
duced as a replacement for any of them or as a complement (i.e., there could
be more than one LMS, SNS or LOR). To provide recommendations in such a
TEL ecosystem, our framework adopts a service oriented architecture. The Rec-
ommender in Fig.1 is implemented as an independent component. It provides a
parameterizable implementation of a graph-based recommender algorithm (1).
The algorithm takes as input a graph with nodes representing items in each of
the platforms and links representing relationships between them (2). The values
given to the nodes and the weights for the edges influence how the algorithm
ranks the elements. A service publishes a function that the platforms can call to
retrieve recommendations (3). All changes in the platforms that are relevant to
compute recommendations (i.e., to build the graph) are communicated (4) and
stored by the recommender in its data model (5). The data model is also the
basis for exchanging relevant data between the platforms and the recommender.
Finally, there is a mapping (6) to generate the graph (i.e., the nodes and edges)
from the data model. The mapping allows for the introduction of links that did
not exist in the data model (e.g., links connecting semantically similar resources
or links that connect users belonging to the same group).
The User Interface and Recommendation Lists: From the user’s perspective,
each platform introduces a recommendation list to the User Interface (UI) com-
ponent. In Fig.2, the recommendation list is shown in Mahara (left side) and
in Moodle (right side). The recommendations are personalized considering the
user’s current focus. For example, in Fig.2 recommendations are provided in Ma-
hara for the user Albert Alonso taking into account that he is currently focused
on viewing Bernard Berazategui’s user profile. Depending on the recommenda-
tion strategy, the recommendation lists might include other users that Bernard
has befriended, activities that he has completed, and resources that he frequently
uses. Consequently, the recommendation lists contain items from any of the three
integrated platforms: Activities, LOs and Users.
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M. Anjorin, I. Dackiewicz, A. Fernández and C. Rensing
Fig. 1. Overview of the Cross-Platform Recommendations Framework
The Service Oriented Approach: The recommender component implements five
core web-services. New resources are added through an addResource() service,
taking as argument the unique identifier (URI) of the object. Attributes of the
object and relationships are added/updated through calls to updateDataAt-
tribute() and updateObjectAttribute() respectively. To retrieve recommenda-
tions, clients call the getRecommendations() service indicating the user and his
current focus (a specific object). To encapsulate the development of the rec-
ommendations in the UI components (thus reducing coupling between these
components and the rest of the functionality of the platforms) we follow a plug-
in approach. Most open platforms support a plug-in extension mechanism. Our
framework provides an interface that plug-ins can invoke to implement opera-
tions to display, register and handle events that correspond to changes to any
of the relevant objects on the platform. Each platform is required to implement
the recommender UI element as a plug-in component, ofcourse, depending on
the platform, this can pose an implementation challenge.
The Data Model and Data Mapping: The data model serves two key purposes:
First, it is used to create the graphs that feed the recommender algorithm. Sec-
ond, it provides basic information about the objects that each of the platforms
displays to the user. This approach has to remain generic enough to accommo-
date not only the platforms that we choose for the proof of concept (Moodle,
Mahara and Ariadne) but other alternatives as well. The data model is stored in
the form of triples. Each object has a unique id (a URI). Relationships between
objects (objectURI, relationship, subjectURI) as well as object attributes (ob-
jectURI, attribute, value) are stored as triples. A certain object attribute relates
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A Framework for Cross-Platform Graph-based Recommendations for TEL
Fig. 2. Recommendation of activities, learning objects and users in Mahara and Moodle
the object to its type (e.g, a user object to the URI of the Person type). The
data model aggregates information that would otherwise be disconnected, e.g,
it connects LOs from the LOR to users and activities in the LMS. Therefore
the definition of a common unique identifier (e.g, primary email for persons)
across all platforms is needed to uniformly identify objects that are present on
the different platforms, and become one in the data model. A challenge here is
considering the access rights the user has in each system in order to only rec-
ommend objects the user is allowed to view. A common user authentication like
single sign-on could be a solution.
The Recommender Algorithm: In this implementation, we choose the PageRank
algorithm on the graph to produce a ranking of nodes. This is implemented using
the JUNG (Java Universal Network/Graph) framework [7]. This ranking is the
basis for the recommendation lists that are returned to clients. A graph mapping
strategy generates the graph from the data model. First, it generates a node for
each object (i.e., Persons, Activities and Resources). Nodes have values (e.g., the
probability of reaching the node after a random jump) and the URI of the object
they represent. A node’s value is set in a way that increases the impact it has in
the resulting ranking, e.g. the node representing the user or the object in focus
starts with a higher weight. Then, the mapping strategy generates edges. The
weight given to each type of edge can be configured to give certain connections
higher relevance. In the current implementation, the relationships considered are
user - user, user - resource, user - activity and activity - resource. The weights
are calculated as the average number of relationships between the different types
RecSysTEL 2012 87
M. Anjorin, I. Dackiewicz, A. Fernández and C. Rensing
of nodes i.e. the number of resources accessed by the user/ the number of re-
sources that have been accessed by any user. In the initial experiment, about 80
relationships are considered between 12 users, 15 LOs and 6 activities.
4 Conclusion
In this paper, we propose to take advantage of the synergies that arise across
multiple platforms in order to generate cross-platform recommendations in a
TEL ecosystem, aiming to further enhance the learning effort of the learners.
Focusing on graph-based recommendations, we discussed design and implemen-
tation challenges. Providing effective recommendations requires experimenting
with different platform combinations, and graph configurations. To ease the de-
velopment efforts, we propose a framework to provide recommendations in a
TEL ecosystem. As a proof of concept and to demonstrate the flexibility of such
a framework, an implementation was made with Moodle as LMS, Mahara as
SNS and Ariadne as LOR. Future work will be to integrate additional platforms
in a different constellation of a TEL ecosystem and to conduct a usability study
to evaluate the recommender algorithms used in the framework.
References
1. Beham, G., Kump, B., Ley, T., Lindstaedt, S.: Recommending knowledgeable people
in a work-integrated learning system. Procedia Computer Science (2010)
2. Boley, H., Chang, E.: Digital Ecosystems: Principles and Semantics. In: Inaugural
IEEE Int. Conf. on Digital Ecosystems and Technologies. Cairns, Australia (2007)
3. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine.
Computer Networks and ISDN Systems 30, 107–117 (1998)
4. Desrosiers, C., G., K.: A Comprehensive Survey of Neighborhood-Based Recom-
mendation Methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Rec-
ommender Systems Handbook, pp. 107–144. Springer (2011)
5. Drachsler, H., Pecceu, D., Arts, T., Hutten, E., Rutledge, L., van Rosmalen, P.,
Hummel, H.G.K., Koper, R.: Remashed - recommendations for mash-up personal
learning environments. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL.
LNCS, vol. 5794, pp. 788–793. Springer (2009)
6. Fernández-Tobı́as, I., Cantador, I., Kaminskas, M., Ricci, F.: A generic semantic-
based framework for cross-domain recommendation. In: Proc. of the 2nd Int. Work-
shop on Information Heterogeneity and Fusion in Recommender Systems. pp. 25–32.
HetRec ’11, ACM, New York, NY, USA (2011)
7. Madadhain, J., Fisher, D., Smyth, P., White, S., Boey, Y.: Analysis and visualization
of network data using jung. Journal of Statistical Software 10, 1–35 (2005)
8. Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender
Systems in Technology Enhanced Learning. In: Ricci, F., Rokach, L., Shapira, B.,
Kantor, P. (eds.) Recommender Systems Handbook, pp. 387–415. Springer (2011)
9. Wenger, E.: Communities of Practice: Learning, Meaning, and Identity. CUP (1998)
RecSysTEL 2012 88