Personalization on the Web of Data and New Paradigms
for Distributed and Open User Modeling
Lora Aroyo
Geert-Jan Houben
VU University Amsterdam
FEW-CS, De Boelelaan 1081
1081 HV Amsterdam, the Netherlands
+31 20 5982868
TU Delft
EWI/ST/WIS, P.O. Box 5031
2600 GA Delft, the Netherlands
+31 15 2787486
[email protected]
[email protected]
ABSTRACT
With more personal data being out there on the Web of Data and
with an increasing level of personalization in the way data is
offered on the Web, the traditional role of user modeling is
changing. Traditionally, user modeling was closely aligned with
the user-adaptation offered by specific adaptive systems, and the
research field of User Modeling reflected this in its assumptions.
For the Web of Data the situation has changed, user modeling has
become both massively distributed and open, and new
assumptions are needed. This has consequences both for the fields
of User Modeling and Adaptive Systems, in terms of their
assumptions and research questions, but also for Web Science as
the role of user model knowledge changes the way the web is
used. Therefore, the many new research challenges in engineering
and studying distributed and open user modeling have to be
approached in the triangle between User Modeling, Adaptive
Web-based Systems, and Web Science.
Keywords
User modeling, adaptive web-based systems, web science, linked
open data, adaptation, personalization, recommendation,
distribution, open.
1. INTRODUCTION
In this paper we reflect on the changes that are awaiting the field
of User Modeling when it tries to meet the demands of
personalization on the Web of Data. With the new nature of the
Web, the desire to make the Web personalized in its offerings to
the large variety of its users asks for new and different solutions.
In traditional Web-based systems the role of the adaptation was
heavily determined by the constraints and assumptions of the
system, and the user modeling that would support this assumption
would operate from the same constraints and assumptions. More
recently, we have already seen research efforts leading to small
steps in the direction of collaboration or integration between
adaptive systems, but in order to obtain true personalization on the
Web of Data a more drastic change is needed.
Just like the Web of Data itself is a consequence of a desire
to move towards a more distributed and open environment of data,
also for user modeling and for user model knowledge we need to
acknowledge that distribution and openness are necessary
ingredients of the new Web. Personalization on the Web of Data
expects the data to be part of a distributed and open environment,
and now also needs to do the same for the user model knowledge
that represents the other side of the coin when it comes to useradaptation and personalization. It is not difficult to see from
current research in user modeling that significant advances of
adaptation and personalization on the Web can only be realized
with truly distributed and open user modeling approaches.
To obtain effective approaches for distributed and open user
modeling, a combined effort is needed from three research fields.
The fields of Adaptive Web-based Systems and User Modeling
obviously need to continue to interact to define the assumptions
and research questions that relate to the use of user model
knowledge for adaptation, but more than ever also the
consequences of distributed and open user modeling on the Web
have to be included in the considerations. Web Science efforts in
this direction include the engineering challenges to deal with
distributed and open user model knowledge on a true Web scale,
but also the study of the many issues (social, legal, ethical,
behavioral, etc.) that follow from a different way of user modeling
on the Web.
Where for example semantics appear to offer great help for
connecting the data on the Web of Data, in order to guide the user
modeling research in the proper effective direction, for example
using semantic techniques in user model representation, in the
triangle between User Modeling, Adaptive Web-based Systems
and Web Science research needs to be connected and targeted to
have a chance to meet the research challenges.
In this paper we take a look back at user modeling and
adaptation in section 2. We then consider the effects of (semantic)
linking data, adaptation and users as the state is now in section 3.
In section 4 we then consider what is ahead and lay out the
challenges that await the research fields, before concluding in
section 5.
2. USER MODELING
In the evolution of the World Wide Web, the Web of documents,
it became quickly apparent that although the hypertext basis for
the Web was a key factor in its success, a single hyperlink
structure of nodes and links would not serve all the people using it
equally well. As a consequence of the goal to adapt the offerings
to the user, the field of adaptive hypermedia started exploring how
the hyperlink basis communicating content to the users could be
made adaptive to each separate user. At first, this was approached
in single closed hyperdocuments and in closed hypermedia
systems, then in open hypermedia systems, and later as part of
systems operating on the Web.
The hyperlink structure that best communicates content to a
user depends on the user’s goal, the user’s context or the user’s
background, to name just a few of the aspects that influence the
user’s reception of hyperlinked documents. An easy example can
be drawn from the field of education, where a teacher designing a
hyperlink structure between documents in order to teach a student
a given subject will consider a number of aspects that determine
the student to decide which pages to create and which links to
create between those pages. An important element in these
considerations of the teacher will be the knowledge that the
student acquires while reading the pages and following the links.
Obviously, the teacher will try to apply known pedagogic
principles to make the student go through the content in
accordance with the knowledge accumulated on the subject and
will therefore create the pages and links to help in that process of
carefully planned knowledge accumulation. It is important to note
that for each link and each page the teacher designs in that
structure, it is essential that the teacher makes an assumption or
pre-condition of the knowledge the student will have when being
faced with that page or link: this knowledge is not just knowledge
acquired before the start of the course, but also the knowledge
obtained while interacting with the content and even the behavior
in that learning process can be relevant for the teacher’s decisions.
Because of the easy-to-understand aspects of adaptation in
educational applications [2], educational examples have
traditionally played an important role in the field of adaptive
hypermedia, but also examples from domains such as tourism and
e-commerce haven drawn a lot of attention from researchers.
The research field of adaptive hypermedia [4] has since long
been the central area for research in making content and
navigation adaptive to the user, and thus turning hypermedia
content into adaptive hypermedia content. This research includes
the creation of systems to provide users with adaptive access to
hypermedia-based content and the analysis of the usage of such
systems and applications. From the start of the area, there has
been a lot of attention to the creation of systems with a restricted
and well-defined scope (in terms of the hypermedia structure): by
locally managing and “knowing” the content and its structure and
by making assumptions about the user’s knowledge and
interaction for that content, it becomes possible to specify exactly
what the adaptive system has to do in the interaction with the user.
In an evolution of this work where adaptive hypermedia systems
met the Web [5,6], we have seen a new generation of adaptive
Web-based systems, where the scoping has been relaxed.
As part of this evolution of adaptive functionality, a lot of
research has in the last two decades been targeting the tooling for
adaptive hypermedia and adaptive web-based systems. In [16] and
its preceding work we have witnessed the consistent extension of
the possibilities to specify and execute user-adaptation in
hypermedia settings. As part of this trend, we can observe how
not only the (expressive) power of the adaptation engines and
mechanisms has been extended, but also how the integration of
content from outside the original scope is supported through
integration and linking.
However, next to creating adaptive solutions and support,
understanding the usage of adaptive hypermedia is also essential
to obtain good user-adaptive hypermedia solutions. Without a
solid understanding of how users experience the adaptation,
adaptive hypermedia cannot effectively be applied as an
instrument in relevant situations. This brings up the connection to
the research area of user modeling, e.g., [7], where the central
focus has been for decades to construct theory and techniques for
gathering and representing user knowledge. For any system that
tries to acknowledge the user’s state (of knowledge, context,
background etc.), whether adaptive hypermedia system or any
other intelligent solution, it is crucial that the system “knows”
who the user is at that moment. User modeling is in this light
perceived as the techniques, often AI-based techniques, that
determine what are relevant attributes of the user for effective
adaptation and how to obtain good values for those attributes. In
domains such as recommendation and teaching user modeling has
advanced enormously and through establishing relevant theories
about the connection between users and adaptation it has become
possible to make relevant user knowledge available for adaptive
applications.
The outcome of the user modeling effort is a user model, and
the link between the fields of user modeling and adaptive
hypermedia [13] is that in the architecture and setup of adaptive
hypermedia systems, a user model plays a crucial role. Until now
we have mainly discussed the possibility to adapt, but this
adaptation is defined on top of a user model. In most adaptive
hypermedia solutions, exemplified by the standard reference
model from [9], this user model exists in an explicit way, as a
model of user knowledge that is explicitly available for the system
to base its adaptation decisions on. So, after “knowing” the user
the system can then concentrate on figuring out how to present
content and navigation for the known user. Following [9], the
general architecture discerns a domain model to describe the
content of the application, a user model to describe the user, and
an adaptation model that describes how domain and user
knowledge are both used to decide about presenting pages and
links.
Figure 1. General Adaptation Architecture
The user model is, as said, an explicit piece of data that
represents the relevant aspects of the user, relevant for adaptation.
Since in many applications the relevant aspects of the user include
the user’s knowledge about the content that is presented in the
application, the systems use most often an overlay model, which
means that the user model is based on the domain model, for
example by associating with each concept in the domain model a
value representing the user’s knowledge about that concept. The
type of knowledge that is typically contained in the user model is
of course depending on the goal and purpose of the application.
The aspects that we see most frequently, although not all at the
same time, i.e. not all in the same application, are: history,
background, preferences, level of knowledge, goals and tasks,
context of work, meta-cognitive skills, personality traits, affective
states, attitudes, etc.
3. LINKING DATA AND USERS
3.1 Web of (Linked) Data
In order to understand the evolution in adaptation, we first
observe the evolution in the Web. From a World-Wide Web of
documents, we have now a moved to a Web of Data. Following
the advances in the Semantic Web and the opportunities offered in
the cloud of Linked Open Data [15], the basis for the content we
concentrate on in our applications has changed. So, the basis for
adaptation has changed. It therefore became straightforward to
investigate how this evolution towards the Web of Data affected
adaptation.
3.2 Linking Data for Adaptation
In Section 2, we have given a background in user-adaptive
systems. When the original adaptive hypermedia systems started
their evolution towards adaptive Web-based systems, the main
advantage with the role of the Web was the interoperability
between applications. It will be obvious that in order to obtain
good adaptive applications, a major investment is needed in terms
of eliciting the relevant knowledge on the domain content, the
users, and the effects of adaptation. The very first aspect where
interoperability could play a beneficial role is in sharing and
reusing of domain content (in the domain model following [9]).
Figure 3. Linking of Users
First of all, inside specific domains such as education it is
easy to see the opportunities for benefits when for users that use a
similar application knowledge is shared between domain-specific
systems and applications [11]. If in this example knowledge about
students can be shared and exploited between learning
management systems over the boundaries of different courses on
the same or related topics, then obviously richer, more and more
relevant, student knowledge can be elicited.
Second, with many Web 2.0 and social networking
applications entering the arena, the sharing of persons and
personal data at a Web scale became a topic. Here we can discern
two aspects: the identification of people, i.e. users, and the
description of people’s attributes, i.e. user properties.
3.3.1 User Identification
For the identification of users across applications, we can rely on
the many initiatives that also serve the wave of social networking
applications in Web 2.0. On the one hand, users themselves can
use identity-based protocols as OpenID to link their various
identities on the Web. On the other hand systems often use
authentication mechanisms, e.g. basic http authentication or open
protocols for secure API authorization, like OAuth.
3.3.2 User Property Representation
Figure 2. Linking of Data
With applications that exploit “external” data from the Web,
the originally closed systems “open up” their content. This was
first started off in the adaptive hypermedia area with work in open
hypermedia systems [3], separating the linking from the content,
and later it became mainstream with content being retrieved from
the Web, often based on metadata in Semantic Web-enhanced
scenarios. It is not difficult to see how the integration of data in
the spirit of the Semantic Web and Linked Data [15] can benefit
the adaptive applications with better content: in that sense these
adaptive applications do not build an exception, and they can use
the many assets that are there to map and align content (in or
across domains).
3.3 Linking Users for Adaptation
Next to the semantic interoperability for the content that is used in
the applications, on the Web it became possible to start sharing
knowledge about the users.
For the representation of user properties the field is still wide
open. To understand the main issues and difficulties here, we need
to know that most user models are overlay models in one way or
another: it means that on top of a domain model it is specified
what is known about the relation between the user and a domain
element. To make it concrete, we can consider an educational
setting where the user model is used to capture a student’s
knowledge on the concepts in a given domain. Then, the user
model will contain knowledge that allows getting an indication of
the knowledge level for each domain concept. It will be clear that
the mapping and alignment of the domain concepts across
different applications, when sharing such user models, reduces to
the normal mapping and alignment of knowledge structures: if the
educational application P about Programming likes to use student
knowledge from an application J about Java, then obviously in the
design of P the relation between concepts from J and concepts
from P will be specified. More specific however for user models
is the problem how to share, exchange and interpret the
knowledge levels: in the example of P and J, it means that P
needs to know how J represents what the students knows about a
given Java concept, before it can decide what this means for its
own representation; if J associates one of the values “passed” and
“not-passed” with each concept and P uses a percentage to
indicate how well a student has learned a concept, then in the
design of P it has to be decided what <C ; ”passed”> means in
terms of a percentage p for a concept D in J. Even from this
simple example we can conclude that here in the mapping and
alignment we have to deal with both (a) the knowledge about the
domain and (b) the knowledge about the knowledge of that
domain: from these the former can be perhaps easily solved in a
particular application domain, but the latter depends heavily on
the specific adaptive systems and their approach to user model
representation. We do stress that in many adaptive systems, the
user modeling is done in a specific way, leading to a great variety
of solutions, not always based on overlay modeling, but overall
the rather specific and often proprietary way of representing the
user properties defines an integration problem.
This lack of interoperability in representing user properties is
one of the biggest hurdles in the integration of adaptive
functionality and personalization at a Web scale.
4. LINKING ADAPTIVE KNOWLEDGE
In the previous section, we have looked at part of the state of
affairs in user model-based adaptation in the evolving Web. In
this section we consider more closely the ambition of integrating
and sharing adaptive and personalization functionality and
knowledge and the requirements and challenges to advance with
that ambition and do so at Web scale. This will help us define the
evolving challenges for user modeling on the Web.
First, we lay out the ambitions and challenges in terms of
adaptation on the Web.
4.1 Adaptation on the Web of Data
On the Web we find many applications that provide adaptive
access and querying to data on the Web. These applications
include sources in many domains and what they have in common
is that the user can interact with them and will be served
individually. Examples can be found in websites that sell books or
music, in applications that recommend TV-programs or travels, in
applications that support education, etc.
These applications typically work separately, and it is left to
a particular application to decide to set up a managed interaction
with other applications to improve and extend its adaptation
capabilities by sharing with other applications. This kind of
managed integration from the side of the application owner and
provider is a standard integration problem that suffers from two
main obstacles:
• Sharing user model knowledge;
• Sharing adaptation functionality.
The latter, sharing adaptation functionality, is the most
complex one, mainly because of the proprietary nature of many of
the adaptation implementation mechanisms being used. In
general, adaptation is realized by a diversity of solutions, and even
when we concentrate on adaptive hypermedia-based solutions that
share more or less the same approach, even then the reuse of
adaptation from one application into the other one remains
complex. The Grapple project [11] is making first steps in that
direction. Motives for sharing adaptation can be found in the aim
to choose the best possible adaptation for a user. When we again
take the example of the educational application, we could imagine
how finding a good and effective adaptation strategy, as a
reflection of an effective learning strategy, is worth being reused
by other applications, that then do not have to go through a longer
process of determining an optimal strategy. Another motive could
come from the delegation of certain adaptation functionality to
specialized components, for example to accommodate the user’s
preferences for adjusting presentations to certain display
properties.
The former, sharing user model knowledge, appears to be a
problem that can reasonably be solved with experience from the
Semantic Web, as for example [1,12,14] demonstrate. This is one
leading scenario for sharing user model knowledge that we
address further on in this paper. Motives for this sharing of user
model knowledge can be found in the fact that cooperation
between applications prevent that an application’s perception of
the user does not need to be built up entirely by the application,
but can be constructed reusing knowledge that other applications
have already gathered: for example, the risk of a “cold start” can
be reduced, and by having more evidence available the possibility
to derive better perceptions increase.
However, besides the benefit for cooperating applications,
there is another starting point that brings us to a similar scenario.
That is when we take the perspective of the individual user that
likes to manage herself the personal information that applications
on the Web use to adapt the interaction to her. Specially, in the
space of social networking sites we have seen first proposals, e.g.
[10], that allow users to control themselves their profiles with
multiple applications.
For both angles to the same scenario, it is necessary to have
ways to share and integrate user model knowledge. Two aspects
that are essential in that respect are distribution and openness. The
process of sharing and integrating user model knowledge can thus
best be described as Distributed and Open User Modeling.
4.2 Distributed and Open User Modeling
In the scenario addressed above, we are confronted with
distributed user model knowledge for which some kind of
combination or integration is asked for. In this section we will
reflect on a number of the research questions and challenges that
need to be addressed to make a significant step forward in
distributed and open user modeling.
For this purpose we dissect the integration problem into its
different elements:
• Identification of the user;
• Alignment of the concepts in the user models;
• Alignment of the user knowledge about the concepts in
the user models.
4.2.1 User Identification
A first problem to solve is the identification of users:
How do we identify a person (or an appearance of a person)?
While this question obviously also was asked in the first
generations of adaptive systems, it will be quickly clear that the
assumptions then and now have changed, and that many more
issues come into a play.
Let us consider, the question from two perspectives. First,
the perspective of a person:
-
How can a person identify himself to an application?
-
How can a person manage his identities (across multiple
applications)?
Second, let us take the perspective of adaptive applications:
-
How can applications find a user (identity) in other
applications (e.g. for the purpose of integrating and
offering a better service)?
Trying to answer these questions brings up issues of trust
and privacy, but also issues from social and behavioral sciences
(when it comes to how people will or will not want to share or
reveal their identities or data), and of course there are legal and
political consequences. Where adaptation providers might have
the best intentions for the users, the way in which they approach
this will be crucial for the success of personalization. It is clear
that the mostly technical solutions that are being proposed in the
fields of User Modeling and Adaptive Web-based Systems are
insufficient in their study of the complete problem. Standards for
representing user identities help, as do tools to manage and map
identities, but the biggest challenge is still wide open and that is
related to all the non-technical aspects that determine
identification.
Distributed and open user identification is a first true Web
Science topic that needs to be put strongly on the research agenda.
4.2.2 Concept Alignment in User Models
After having established user identification, sharing and
exchanging user model knowledge starts with concept alignment:
How is conceptual knowledge integrated and shared?
It is not difficult to see how this challenge reduces to
standard concept mapping and alignment if we just look at the
techniques. From a technical point of view, there is nothing
special, i.e. which is not already studied in Web of Data research.
Of course, for the purpose of subsequently being able to foster
more sharing of user model knowledge and therefore provide
better adaptive services, obviously any attempt to do more reuse
and sharing in data and knowledge, e.g. using SKOS, RDF/OWL
etc., is beneficial. So, here we do not elicit any specific
challenges, but we do observe how a semantic-based integration
of data (concepts) can improve the chances for sharing user model
knowledge about the data.
standard vocabulary for these properties is not an impossible task.
So, this leads to the following questions:
-
Which are the properties that are typically found in user
model knowledge?
-
How can we represent these user properties?
Of course, a start from the common core of properties that
are known from current systems does not imply that on the open
Web things could not be different, e.g. some user properties only
show up on the open Web, but with a common core it becomes
possible to study those effects. So, after the initial knowledge
representation challenge, Web Science can kick in to study the
effects at the scale of the Web.
Note that using a standardized representation does not say
that all applications access and use one and the same user profile.
Allowing to have a shared vocabulary should not be mixed with
having one profile for all applications. However, with the shared
vocabulary and the possibilities to align user model knowledge,
besides the technical issues, there is the obvious need to study the
implications regarding trust, privacy, etc., to be able to find a way
of working that satisfies all parties that (can, may and do) decide
to connect.
User knowledge alignment is a second true Web Science
topic that needs to be put strongly on the research agenda.
4.2.4 Openness and scrutability
Most of the considerations in the previous subsections relate to the
distribution of users and user knowledge. Closely connected is the
openness that comes with the Web of Data. Where this Web has
evolved from the exploitation of open data, one can now consider
the question how open user knowledge can be:
How can user knowledge be made open to allow systems and
applications to use it for personalization?
How can user model knowledge be shared between adaptive
applications?
This main question brings up many challenging problems,
for which researchers need to find the answers. We stress that
these answers are not the technical solutions that show how user
knowledge could be published openly if we would forget about all
legal, ethical etc. issues: it is exactly the non-technical part of the
problem that should be leading in the study of how openness in
user modeling can benefit personalization. Here, new paradigms
are needed to understand how users and personalization providers
perceive their mutual benefits. These new benefits might then
require new technical solutions, but at first sight it appears that
Web Science first needs to address how the publication of user
knowledge can serve the purpose of the user.
The most promising answer to this question implies that user
model knowledge, even when gathered through very diverse
techniques, should have a standard semantic representation. In
that representation, e.g. through annotation, we then include for
all user model knowledge the necessary provenance information
about the user model knowledge. It is that (user model-specific)
provenance information that helps to communicate what the user
model knowledge represents. Then, this representation can be
used to exchange between applications. Technically, this would
not be difficult, but determining the relevant provenance
information is a wide open issue.
One aspect of open publication relates to a fundamental
element of user modeling: scrutability. There should be a close
interaction between the user and the system that maintains user
knowledge about the user. Next to the control the user has to have
of the user knowledge as it is shared or not shared between
applications, it is also considered important to what level the user
is able to inspect and alter her own user model knowledge.
Obviously, there will be open and proprietary (to the provider)
user knowledge, but system-specific mechanisms to elicit and
verify user knowledge, e.g. [8], are to be extended for use in the
context of the distributed and open Web.
Obviously, there will be no standard representation that fits
with all of the current tools and applications, but if we look at user
modeling theories and experience we can see that the properties
that are usually included (in studies in closed applications and
systems) are more or less known for most cases, and to create a
The openness of user knowledge is a third true Web Science
topic that needs to be put strongly on the research agenda.
4.2.3 User Knowledge Alignment in User Models
With users identified and data aligned, the third major step is the
alignment of user knowledge, i.e. the relation between the user
and the data.
The main question here is:
5. CONCLUSION
In this paper we have taken a look at the current state of affairs in
user modeling, and from the challenges that personalization on the
Web of Data implies, we have derived a number of research
questions that should be high on the research agenda. The three
main subjects for further research are distributed and open user
identification, user knowledge alignment, and openness of user
knowledge.
At the same time, we have shown how the traditional
collaboration between the fields of User Modeling and Adaptive
Web-based Systems could and should collaborate more
intensively with Web Science. In that triangle it becomes possible
to exploit the large number of theories and results for user
modeling and adaptation on the new Web and study how well
they perform, while also providing a good basis for investigating
new paradigms for distributed and open user modeling.
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