RESTful User Model API for the Exchange of User’s Preferences among
Adaptive Systems
Martin Bal´ık and Ivan Jel´ınek
Department of Computer Science and Engineering, Faculty of Electrical Engineering,
Czech Technical University, Karlovo n´amˇest´ı 13, 121 35 Prague, Czech Republic
Adaptive Hypermedia, Personalization, User Model, Data Exchange, REST API.
Adaptive Hypermedia Systems observe users’ behavior and provide personalized hypermedia. Users interact
with many systems on the Web, and each user-adaptive system builds its own model of user’s preferences and
characteristics. There is a need to share the personal information, and the current research is exploring ways to
share user models efficiently. In this paper, we present our solution for personal data exchange among multiple
hypermedia applications. First, we designed a communication interface based on the REST architectural style,
and then, we defined data structures appropriate for the data exchange. Our user model is ontology-based and
therefore, the data from multiple providers can be aligned to achieve interoperability.
Hypermedia applications on the World Wide Web
(WWW) are used by people on a daily basis. Peo-
ple use applications intended for work, entertainment,
communication, learning, etc. To improve usability
and facilitate the orientation within the large amount
of information, many of the applications provide auto-
mated personalization and adaptation features. Such
applications are called AdaptiveHypermedia Systems
(AHS) (Brusilovsky, 2001; Knutov et al., 2009) and
belong to the category of user-adaptive systems.
User-adaptive systems monitor users’ behavior
and keep track of characteristics, behavior, prefer-
ences, etc. of each individual user. The collection of
personal data associated with a specific user is called
the User Model (UM). The User Model is typically
built individually within each user-adaptive applica-
tion. This can cause a lot of issues, including the
cold start problem (Salton and McGill, 1983), infor-
mation inconsistency (Vassileva et al., 2003), both-
ering users with initial setup and asking for similar
data input, narrow personal information domain, etc.
In our work, we focus specifically on Adapitve Hy-
permedia Systems (AHS). Many different AHSs are
used by a single person on a daily basis. The effort of
extracting user’s characteristics is commonly repeated
in multiple self-contained applications. Moreover, in-
dividual user models can include complementary in-
formation. Therefore, it would be very beneficial to
share the personal information and keep it synchro-
nized between all applications used by the person.
Sharing a User Model is a required, but a very
challenging task. Fortunately, the users are willing
to share personal information (Kobsa and Teltzrow,
2006; Gross and Acquisti, 2005). It is important
to find a balance between information revelation and
personal privacy. The convenient choice of informa-
tion providers and proper integration of data sources
will bring many benefits to the user.
The user usually works with diverse services on
the Web. Sometime, even the domain is quite similar.
In other cases, there can be connections with other
people with corresponding interests and new links to
reusable data. On the web, everything is intercon-
nected. Though, adding semantics to the raw data
brings new possibilities how the data can be used.
Through semantic annotations, the vision of Linked
Data (Bizer et al., 2009) is becoming true.
The main problem of personal information shar-
ing is the heterogeneity of User Models. To deal with
the interoperability problems, especially the semantic
heterogeneity of User Models needs to be addressed
(Carmagnola, 2009). Users’ data are stored in AHSs
in different formats and use different syntactic and
conceptual structures. Solving the AHS interoperabil-
ity problems by utilizing an Application Program In-
terface (API) is an appropriate research direction as
discussed in the context of adaptive educational sys-
tems in (Aroyo et al., 2006).
Balík M. and Jelínek I..
RESTful User Model API for the Exchange of User’s Preferences among Adaptive Systems.
DOI: 10.5220/0004896806270634
In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 627-634
ISBN: 978-989-758-028-4
2014 SCITEPRESS (Science and Technology Publications, Lda.)
The paper proposes a new adaptive hypermedia
system architecture and a method of personal data ex-
change. RESTful web service is used to expose both
user’s preferences and through user modeling process
obtained characteristics.
The paper is structured as follows. Section 1 deals
with the description of AHS and the tasks to solve. In
Section 2, a current state of the art of the discussed
topic is being reviewed. In Section 3, both the pro-
posed personal data storage structure and the RESTful
user model integration interface are described in de-
tail. In Section 4, a use case scenario utilizing the ben-
efits of the RESTful user model interface is demon-
strated. Finally, the paper concludes by summarizing
results of the research and indicates the directions of
the future work; see Section 5.
User-adaptive applications are fundamentally based
on a process of observing user’s behavior and stor-
ing relevant user-specific information. This process
is called user modeling, and data is stored into a
user model. The user modeling process is a marginal
topic for the presented research, and an overview of
existing user modeling techniques can be found in
(Sosnovsky and Dicheva, 2010; Aroyo and Houben,
2010). In next subsections, we will first review exist-
ing approaches used to share user-specific informa-
tion stored in the user model, and second, we will
explain fundamentals of the Web architecture, Web
services and Representational State Transfer.
2.1 User Model Sharing Approaches
Recently, motivated by the expansion of mobile and
ubiquitous devices, the researchers noticed the need
of sharing personal profiles of users, to enhance the
adaptation abilities of user-adaptiveapplications. Var-
ious techniques of sharing personal data have been
proposed (Carmagnola, 2009; Sosnovsky et al., 2009;
Cena and Furnari, 2009). The main obstacle is usu-
ally to solve the data interoperability problems. There
are currently two main approaches. The first possibil-
ity is the way of standardization. The systems need
to adhere to a fixed representation that needs to be re-
spected by all service providers. The second approach
is using mediation techniques to transfer the data from
one representation to another.
Standardization-based approach of AHS integra-
tion assumes a common semantic representation of
user models within all participating systems, usually
expressed by a shared ontology (Heckmann et al.,
2005). Implementing domain models of adaptive sys-
tems as ontologies is the first step toward interoper-
ability. A standardized user-modeling ontology is a
possible solution to make the information exchange
possible. However, the fundamental requirement of
this approach is that all participants agree upon the
standardized ontology, which may pose an issue for
some of them (Berkovsky et al., 2009).
As a generalization of a standardization-based ap-
proach, the central user modeling server can be as-
sumed. A solution, where adaptive systems do not
need to support user modeling was used in (Kay
et al., 2002).The networked adaptive applications act
as clients, they simultaneously update the central user
model on the server, and they can request back per-
sonal information when needed. Another solution
based on a central server is presented in (van der
Sluijs and Houben, 2005), where the exchange of user
data between applications is supported by Semantic
Web technologies. The authors call the component
providing user model storage the Generic User Mod-
eling Component.
More complex solutions than standardization are
utilized by mediation (Berkovsky et al., 2007). With-
out any standard vocabulary, it is necessary to solve
syntactic and semantic heterogeneity issues. Ontol-
ogy mediation is the process of reconciling differ-
ences between heterogeneous ontologies in order to
achieve inter-operation between data sources anno-
tated with and applications using these ontologies
(de Bruijn et al., 2005). To overcome the hetero-
geneity of user modeling data, two steps are required.
First, the reasoning and inference mechanisms for
converting data between various representations, ap-
plications and domains need to be developed and ap-
plied. Second, the semantically enhanced knowledge
bases are exploit, facilitating the above reasoning and
inference (Berkovsky et al., 2009).
Although, there have been projects like (Heck-
mann et al., 2005) primarily focusing on the standard,
widely accepted user modeling ontology, (Martinez-
Villase˜nor et al., 2012) claims that the standardization
approach is not a feasible solution. Such statement
suggests to follow the second direction, the media-
tion of different domains based on natural language
processing and artificial intelligence. However, there
is also a possibility of a hybrid approach combining
both standardization and mediation approaches. Such
unification is the aim of our approach.
2.2 The Web Architecture
Representational State Transfer (REST) (Fielding and
Taylor, 2002) architectural style was proposed by
Table 1: Vocabulary namespaces used in the REST API.
Prefix Namespace URI Description
dcterms: Dublin Core vocabulary
foaf: Friend of a Friend (FOAF) vocabulary
sioc: Semantically-Interlinked Online Communities (SIOC) vocabulary
owl: Web Ontology Language (OWL) vocabulary
rdf: RDF vocabulary
rdfs: RDF Schema vocabulary
xsd: XML Schema (XSD) vocabulary
um: IntelLEO User Model Ontology
gomawe: GOMAWE Architecture Ontology
Roy Fielding and is based on a set of principles
for designing network-based software architectures.
The set of principles was defined by four interface
constraints: identification of resources, manipulation
of resources through representations, self-descriptive
messages, and hypermedia as the engine of applica-
tion state.
Web services based on the principles of REST
are called RESTful and can be considered as an al-
ternative to the SOAP-based web services. In the
past, RESTful services were used only for simple ad-
hoc services, and the area of enterprise systems was
scoped to the WS-* standards, e.g., SOAP, WSDL,
WS-Addressing, WS-Security. This is no longer the
case and RESTful services have been successfully ap-
plied in many enterprise applications. The challenge
is to use them correctly and to be able to align them
to solve the real problems (Adamczyk et al., 2011).
The comprehensive comparison of both technologies
was presented in (Pautasso et al., 2008; Pautasso and
Wilde, 2009). Conclusions of the comparison give
an advantage to the RESTful services in Web inte-
gration scenarios and prefer WS-* Web services for
enterprise application integration, where advanced se-
curity and Quality of Service (QoS) is required. The
practical comparison in (Guinard et al., 2011) con-
cludes that REST is lightweight, scalable, very easy
to understand, learn, and implement.
There are two important terms when speaking
about REST methods safety and idempotence
(Fredrich, 2012). Safety means that calling the
method does not cause side effects and does not
change the state of the server. For example, the
method of an API must adhere to the safety definition,
otherwise it can cause problems for other services and
result in unintended changes on the server. Idempo-
tence refers to a method that will produce the same
results if executed once or multiple times. The
methods are defined to be idempotent.
Therefore, it should be ensured that making multi-
ple requests produce the same result on the server.
Safe methods are idempotent at the same time, be-
cause they do not cause any changes on the server.
In our previous work, we have formalized the Generic
Ontology-based Model for Adaptive Web Environ-
ments (GOMAWE) (Bal´ık and Jel´ınek, 2008). This
model defines the fundamental components of an
adaptive hypermedia application. One of the compo-
nents is the integration interface intended for expos-
ing user-specific data in the local storage to external
applications, and, at the same time, for obtaining user-
specific data from external providers.
Based on the GOMAWE formal specification,
the Adaptive System Framework (ASF) (Bal´ık and
Jel´ınek, 2013) was built to support AHS development.
ASF provides the most typical AHS components serv-
ing as building blocks for further development. The
most recent extension of the framework is the inte-
gration component based on the principles described
in this article.
3.1 User Model
In our design, the user model has a special architec-
ture that was devised from the requirements, includ-
ing types of stored information and methods of in-
formation retrieval. We divided the user data into two
parts the User Profile and User Model. This division
corresponds to explicit and implicit personalization
styles. Implicit personalization is performed by the
adaptivesystem. On the other hand, explicit personal-
ization is performed by the user using special features
of the system. A system with such personalization
features is called adaptable system. Our architecture
can be perceived as a hybrid solution combining both
personalization methods.
The User Profile contains explicit user’s prefer-
ences, i.e., preferences explicitly filled by the users.
This data is stored as key-valuepairs, see Definition 1.
xsd:string xsd:stringxsd:string
Figure 1: Selected parts of the GOMAWE Architecture Ontology.
The key is usually a constant string defined by the de-
veloper of the application. Typical origin of the data
is the “settings page” of the application. However,
when we assume integration with other personal data
providers, the User Profile data can be filled utiliz-
ing social networks’ user profiles available in services
like Facebook, Twitter or LinkedIn.
Definition 1 (User Profile). The User Profile of a user
u is a tuple UP
= (A, V, r)
r : A V|∀a A : r
, (1)
where A is a set of attributes defined as the vocabu-
lary of user’s characteristics, V =
is a set of
attribute values and V
is the range of attribute a.
The User Model denotes in our terminology
a model containing implicit user’s characteristics.
While the explicit characteristics are set by the user
oneself, the implicit characteristics are devised by the
adaptive system. The Domain Model captures the
most important types of objects in the application
context. It defines the conceptual framework and se-
mantics of hypermedia content. The system collects
various values related to a specific object of the do-
main and stores the values into the User Model, see
Definition 2. Moreover, the characteristics are cate-
gorized to a set of dimensions. The assignment of
attributes and dimensions is utilized in the user mod-
eling process, however, it will not be discussed here
as it is not substantial for the web services and the
application integration interface.
Definition 2 (User Model). The User Model of a user
u is a tuple UM
= (D, A, V, r)
r : D× A V|∀a D× A : r
, (2)
where D is a set of domain instances, A is a set of
attributes defined as the vocabulary of user’s implicit
characteristics, V =
is a set of attribute values
and V
is the range of attribute a.
As examples of User Model attributes we can
mention knowledge of a lesson in an educational ap-
plication, or bought item indication in a web com-
merce application. The User Model is able to capture
even relationships among users. Based on the fact that
a user entity is part of the Domain Model, the User
Model can include attributes friend, follower, etc. re-
lating the user to other users. This information can be
acquired from social networking applications.
3.2 A RESTful User-specific Data
Based on the comparison in Section 2, we decided to
use RESTful web services to design the user-adaptive
application’s personal data interface. First, the inter-
face does not require advanced security, and second,
the most important requirement is flexibility supple-
mented with easy and intuitive development.
The exchange of information is based on standard
metadata vocabularies and ontologies. Utilizing the
proposed unified user model data structure, ontolo-
gies can be aligned with relative ease and transla-
tion between two domains can be achieved. At the
same time, the design does not force participating sys-
tems to agree fully on a fixed domain model ontology,
and advanced mediation techniques can be used when
3.2.1 RDF Vocabularies
Our user-data-integration interface uses three types
of vocabularies first, the standard vocabularies,
second, user-model-specific vocabularies and finally,
the domain-specific vocabularies. RDF resources
and their attributes reuse existing, widely adopted
vocabularies such as the Dublin Core (Dub, ),
Friend of a Friend (Brickley and Miller, 2010), and
Semantically-Interlinked Online Communities (Sio, )
Client REST
Integration service
User Model
User Profile
GET /api/users/lookup?
GET /api/users/{user_id}/profile
getAl lAttributes(user_id)
:List<UserProfil eAttribute>
GET /api/users/{user_id}/model?
user_id, dateT ime)
getAl lAttributes(
user_id, dateT ime)
:List<UserModel Attribute>
Figure 2: Example of request sequence to retrieve users’s personal data.
vocabularies. To ensure good interoperability, ASF
maps as many attributes as possible to these standard
vocabularies. User-model-specific vocabularies used
in our design include IntelLEO User Model Ontol-
ogy (Jovanovic et al., 2012) and GOMAWE Archi-
tecture Ontology, see Fig. 1. Domain-specific vocab-
ularies differ application from application and refer to
domain-specific concepts to which the user’s charac-
teristics are related.
The Table 1 lists the vocabulary namespaces used
in the ASF-based application REST API.
3.2.2 ASF-based Application API Resources
The fundamental resources of the RESTful API are
listed in Table 2.
Table 2: Primary resources of the REST API.
Type Example
gomawe:UserProfileAttribute /api/users/{user id}/profile
gomawe:UserModelAttribute /api/users/{user id}/model
1) /api/users/{user id}
2) /api/users/lookup?email={emailAddress}
In Table 2,
refers to the list of User Profile attributes as-
signed to the user with the matching ID.
refers to the list
of User Model data of the user with the matching
refers to the owner of the user
account corresponding to the specified id (1) or email
address (2).
3.2.3 REST Operations
This subsection summarizes the REST operations
supported by the API. The API supports three HTTP
. The
is not supported, as the user data can only be extended
or updated, and no attributes can be removed.
Only the RDF/XML format is supported at this
time, therfore the HTTP Accept header value should
be set to:
GET /api/users HTTP/1.1
Accept: application/rdf+xml
The following parameters can be used to refine the
Modified since Parameter
be used to limit the listed attributes to only those
that were modified after the entered time value.
The parameter value is of type
Resource paging Parameter
can be used
to limit the number of listed attributes to avoid
large data transfers. Parameter
can be
used to request additional pages starting with the
specified item. For both parameters integer type
value is allowed.
The following are the typical uses of HTTP meth-
ods with explanatory examples:
method is supported
by all API resources. It is used to retrieve the
user’s account and his or her user modeling data.
First, the application needs to negotiate the cor-
rect resource IDs and after matching user’s ac-
count, the user model attributes can be requested,
see Fig. 2. The following request can be used to
retrieve user profile attributes of a user:
{user_id}/profile HTTP/1.1
Accept: application/rdf+xml
method is supported
only by UserProfile and UserModel resources. It
is used to update the models by an external ap-
plication. The
method operation needs to be
idempotent, therefore, using this method means
creating new attributes or replacing values of ex-
isting attributes.
{user_id}/profile HTTP/1.1
method is sup-
ported only by UserProfile and UserModel re-
sources. The operation of this method is reserved
for the cases of incremental updates of the models.
It will be more extensivelyutilized in future exten-
sions of the REST API. The more precise value
updates can be based on arithmetic mean or the
time of last update. This operation is not idempo-
tent and should be called only once to avoid inap-
propriate user model changes.
3.2.4 Authentication
To avoid misuse of both the personal and the domain
data provided by the RESTful application interface,
authentication of requests is required. We use HTTP
basic authentication. Using HTTPS protocol is rec-
ommended. Otherwise, the username and password
would be sent without encryption.
The proposed approach of AHS integration will be
demonstrated in an adaptive learning scenario. There
are currently three separate systems used by students
in a programming course, each with a different pur-
pose. Although each of the systems stores different
information, they can benefit from each other, ex-
change user’s data and extend understanding of the
user’s knowledge and preferences.
The integration module implementation is a work-
in-progress project, and the results will be evaluated
in the future. Currently, we do not focus on ontol-
ogy mapping issues. Our aim is to verify the web
service interface and the method of exposing user-
specific personal data.
There are three participating information systems
in our scenario. The first system is an adaptive learn-
ing system containing learning materials and simple
test questions at the end of each lesson. The sec-
ond system is intended for selection of a program-
ming project topic and for submission of the com-
pleted works. The third system performs an auto-
matic evaluation of several programming tasks as-
signed to students in the course of a year. The adap-
tation features of the learning system can benefit to
a considerable extend by additional information about
students’s progress on solving the assigned tasks and
their achieved results. The adaptive guidance within
the learning course can be also really well tailored
based on the student’s project topic, and the subtopics
related to his or her project.
In the current setup, see Fig. 3, each of the sys-
tems is equipped with the RESTful data interface, and
all systems are interconnected through a central medi-
ation service. Each of the systems conforms to a sim-
ilar domain, and their domain models overlap with
some mutually related concepts.
The User Model Attributes exchange is subject to
a certain level of mutual understanding of the domain
semantics by the communicating counterparts. In our
experimental scenario, for the ease of personal-data
interface demonstration, all three systems use the do-
main ontology of the adaptive learning system. The
data structure defined by the ontology is mapped lo-
cally. Furthermore, the RESTful API of the learn-
ing systems is extended by the domain resources and
systems can negotiate the correct domain instance
references. All domain instances are represented as
RDFS-resources identified by a unique Uniform Re-
source Identifier (URI). The URIs are used as a link
between the overlay User Model and the domain layer
of the application.
The User Profile Attributes are not domain-
Service Mediator
Adaptive Learning System
Project assignment
Programming tasks
ASF Extension
ASF Extension
Figure 3: Applications participating in the use-case integra-
tion scenario.
dependent. The meaning of the included prefer-
ences needs to be well represented using common
and widely used ontologies. In later implementations,
public services like Facebook, Twitter or LinkedIn
can be used as user’s personal information providers,
and therefore, it is important to be able to match the
preferences adequately.
The paper has proposed our novel solution to deal
with the syntactic and semantic heterogeneity of per-
sonal information in adaptive hypermedia systems.
To make the integration of users’ preferences and
characteristics possible, a special architecture of User
Profile and User Model was defined, and a RESTful
web service application interface was introduced.
Compared to other solutions presented in Sec-
tion 2, our proposal combines both shared format and
conversion approaches resulting in a hybrid solution
and utilizing advantages of both approaches.
The strength of our approach is the generic ar-
chitecture, and the fact that realization can be sup-
ported by the Adaptive System Framework. Even
non-adaptive systems can be extended by the ASF-
based integration module and operate as providers
of users’ personal data. An integration use-case of
that kind was presented using the case of multiple
learning-support applications. As a result, the adap-
tive learning application can benefit from the integra-
tion and promptly react on achievements of students
recorded by related learning-support systems.
In our future work, we will thoroughly evaluate
the integration impact on a deeper understanding of
user’s knowledge, preferences and needs.
The special user modeling data storage and the in-
troduction of the web-service-based application inter-
face of a user-adaptive application is one of the im-
portant steps towards the generic AHS architecture
The results of our research form a part of the scientific
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