| Coming of age: celebrating a quarter century of user modeling and personalization: Guest editors' introduction | | BIB | Full-Text | 1-7 | |
| Judy Kay; Gord McCalla | |||
| A review of recent advances in learner and skill modeling in intelligent learning environments | | BIBAK | Full-Text | 9-38 | |
| Michel C. Desmarais; Ryan S. J. d. Baker | |||
| In recent years, learner models have emerged from the research laboratory
and research classrooms into the wider world. Learner models are now embedded
in real world applications which can claim to have thousands, or even hundreds
of thousands, of users. Probabilistic models for skill assessment are playing a
key role in these advanced learning environments. In this paper, we review the
learner models that have played the largest roles in the success of these
learning environments, and also the latest advances in the modeling and
assessment of learner skills. We conclude by discussing related advancements in
modeling other key constructs such as learner motivation, emotional and
attentional state, meta-cognition and self-regulated learning, group learning,
and the recent movement towards open and shared learner models. Keywords: Student models; Learner models; Probabilistic models; Bayesian Networks;
IRT; Model tracing; POKS; Bayesian Knowledge Tracing; Intelligent Tutoring
System; Learning environments; Cognitive modeling | |||
| Fifteen years of constraint-based tutors: what we have achieved and where we are going | | BIBAK | Full-Text | 39-72 | |
| Antonija Mitrovic | |||
| Fifteen years ago, research started on SQL-Tutor, the first constraint-based
tutor. The initial efforts were focused on evaluating Constraint-Based Modeling
(CBM), its effectiveness and applicability to various instructional domains.
Since then, we extended CBM in a number of ways, and developed many
constraint-based tutors. Our tutors teach both well- and ill-defined domains
and tasks, and deal with domain- and meta-level skills. We have supported
mainly individual learning, but also the acquisition of collaborative skills.
Authoring support for constraint-based tutors is now available, as well as
mature, well-tested deployment environments. Our current research focuses on
building affect-sensitive and motivational tutors. Over the period of fifteen
years, CBM has progressed from a theoretical idea to a mature, reliable and
effective methodology for developing effective tutors. Keywords: Constraint-based modeling; Constraint-based tutors; Authoring; Affective
modeling; Metacognitive skills; Collaborative learning | |||
| Personalization in cultural heritage: the road travelled and the one ahead | | BIBAK | Full-Text | 73-99 | |
| Liliana Ardissono; Tsvi Kuflik | |||
| Over the last 20 years, cultural heritage has been a favored domain for
personalization research. For years, researchers have experimented with the
cutting edge technology of the day; now, with the convergence of internet and
wireless technology, and the increasing adoption of the Web as a platform for
the publication of information, the visitor is able to exploit cultural
heritage material before, during and after the visit, having different goals
and requirements in each phase. However, cultural heritage sites have a huge
amount of information to present, which must be filtered and personalized in
order to enable the individual user to easily access it. Personalization of
cultural heritage information requires a system that is able to model the user
(e.g., interest, knowledge and other personal characteristics), as well as
contextual aspects, select the most appropriate content, and deliver it in the
most suitable way. It should be noted that achieving this result is extremely
challenging in the case of first-time users, such as tourists who visit a
cultural heritage site for the first time (and maybe the only time in their
life). In addition, as tourism is a social activity, adapting to the individual
is not enough because groups and communities have to be modeled and supported
as well, taking into account their mutual interests, previous mutual
experience, and requirements. How to model and represent the user(s) and the
context of the visit and how to reason with regard to the information that is
available are the challenges faced by researchers in personalization of
cultural heritage. Notwithstanding the effort invested so far, a definite
solution is far from being reached, mainly because new technology and new
aspects of personalization are constantly being introduced. This article
surveys the research in this area. Starting from the earlier systems, which
presented cultural heritage information in kiosks, it summarizes the evolution
of personalization techniques in museum web sites, virtual collections and
mobile guides, until recent extension of cultural heritage toward the semantic
and social web. The paper concludes with current challenges and points out
areas where future research is needed. Keywords: Personalized access to cultural heritage; Personalization; Cultural heritage | |||
| Recommender systems: from algorithms to user experience | | BIBAK | Full-Text | 101-123 | |
| Joseph A. Konstan; John Riedl | |||
| Since their introduction in the early 1990's, automated recommender systems
have revolutionized the marketing and delivery of commerce and content by
providing personalized recommendations and predictions over a variety of large
and complex product offerings. In this article, we review the key advances in
collaborative filtering recommender systems, focusing on the evolution from
research concentrated purely on algorithms to research concentrated on the rich
set of questions around the user experience with the recommender. We show
through examples that the embedding of the algorithm in the user experience
dramatically affects the value to the user of the recommender. We argue that
evaluating the user experience of a recommender requires a broader set of
measures than have been commonly used, and suggest additional measures that
have proven effective. Based on our analysis of the state of the field, we
identify the most important open research problems, and outline key challenges
slowing the advance of the state of the art, and in some cases limiting the
relevance of research to real-world applications. Keywords: Recommender systems; User experience; Collaborative filtering; Evaluation;
Metrics | |||
| Critiquing-based recommenders: survey and emerging trends | | BIBAK | Full-Text | 125-150 | |
| Li Chen; Pearl Pu | |||
| Critiquing-based recommender systems elicit users' feedback, called
critiques, which they made on the recommended items. This conversational style
of interaction is in contract to the standard model where users receive
recommendations in a single interaction. Through the use of the critiquing
feedback, the recommender systems are able to more accurately learn the users'
profiles, and therefore suggest better recommendations in the subsequent
rounds. Critiquing-based recommenders have been widely studied in knowledge-,
content-, and preference-based recommenders and are beginning to be tried in
several online websites, such as MovieLens. This article examines the
motivation and development of the subject area, and offers a detailed survey of
the state of the art concerning the design of critiquing interfaces and
development of algorithms for critiquing generation. With the help of
categorization analysis, the survey reveals three principal branches of
critiquing based recommender systems, using respectively natural language
based, system-suggested, and user-initiated critiques. Representative example
systems will be presented and analyzed for each branch, and their respective
pros and cons will be discussed. Subsequently, a hybrid framework is developed
to unify the advantages of different methods and overcome their respective
limitations. Empirical findings from user studies are further presented,
indicating how hybrid critiquing supports could effectively enable end-users to
achieve more confident decisions. Finally, the article will point out several
future trends to boost the advance of critiquing-based recommenders. Keywords: Critiquing-based recommenders; Survey; Preference elicitation; Example
critiquing; Dynamic critiquing; Hybrid critiquing; User evaluations | |||
| Discovery of Web user communities and their role in personalization | | BIBAK | Full-Text | 151-175 | |
| Georgios Paliouras | |||
| One of the major innovations in personalization in the last 20 years was the
injection of social knowledge into the model of the user. The user is not
considered an isolated individual any more, but a member of one or more
communities. User communities have been facilitated by the striking
advancements of electronic communications and in particular the penetration of
the Web into people's everyday routine. Communities arise in a number of
different ways. Social networking tools typically allow users to proactively
connect to each other. Alternatively, data mining tools discover communities of
connected Web sites or communities of Web users. In this article, we focus on
the latter type of community, which is commonly mined from logs of users'
activity on the Web. We recall how this process has been used to model the
users' interests and personalize Web applications. Collaborative filtering and
recommendation are the most widely used forms of community-driven
personalization. However, we examine a range of other interesting alternatives
that are worth investigating further. This effort leads us naturally to the
recent developments on the Web and particularly the advent of the social Web.
We explain how this development draws together the different viewpoints on Web
communities and introduces new opportunities for community-based
personalization. In particular, we propose the concept of active user community
and show how this relates to recent efforts on mining social networks and
social media. Keywords: User communities; Web mining; Web personalization; Web communities; Social
networks | |||
| Motivating participation in social computing applications: a user modeling perspective | | BIBAK | Full-Text | 177-201 | |
| Julita Vassileva | |||
| The explosive growth of Web-based social applications over the last 10 years
has led people to engage in online communities for various purposes: to work,
learn, play, share time and mementos with friends and family and engage in
public action. Social Computing Applications (SCA) allow users to discuss
various topics in online forums, share their thoughts in blogs, share photos,
videos, bookmarks, and connect with friends through social networks. Yet, the
design of successful social applications that attract and sustain active
contribution by their users still remains more of an art than a science. My
research over the last 10 years has been based on the hypothesis that it is
possible to incorporate mechanisms and tools in the design of the social
application that can motivate users to participate, and more generally, to
change their behavior in a desirable way, which is beneficial for the
community. Since different people are motivated by different things, it can be
expected that personalizing the incentives and the way the rewards are
presented to the individual, would be beneficial. Also since communities have
different needs in different phases of their existence, it is necessary to
model the changing needs of communities and adapt the incentive mechanisms
accordingly, to attract the kind of contributions that are beneficial.
Therefore User and Group (Community) Modeling is an important area in the
design of incentive mechanisms. This paper presents an overview of different
approaches to motivate users to participate. These approaches are based on
various theories from the area of social psychology and behavioral economics
and involve rewards mechanisms, reputation, open group user modeling, and
social visualization. Future trends are outlined towards convergence with the
areas of persuasive systems design, adaptive/personalized systems, and
intelligent social learning environments. Keywords: Social computing; Participation; Motivation; Persuasion; Gamification; Open
user models; Group user models; Reflection; Adaptive incentive mechanism;
Incentives; Mechanism design | |||
| Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems | | BIBAK | Full-Text | 203-220 | |
| Eran Toch; Yang Wang; Lorrie Faith Cranor | |||
| Personalization technologies offer powerful tools for enhancing the user
experience in a wide variety of systems, but at the same time raise new privacy
concerns. For example, systems that personalize advertisements according to the
physical location of the user or according to the user's friends' search
history, introduce new privacy risks that may discourage wide adoption of
personalization technologies. This article analyzes the privacy risks
associated with several current and prominent personalization trends, namely
social-based personalization, behavioral profiling, and location-based
personalization. We survey user attitudes towards privacy and personalization,
as well as technologies that can help reduce privacy risks. We conclude with a
discussion that frames risks and technical solutions in the intersection
between personalization and privacy, as well as areas for further
investigation. This frameworks can help designers and researchers to
contextualize privacy challenges of solutions when designing personalization
systems. Keywords: Privacy; Personalization; Human--computer interaction; Social networks;
E-commerce; Location-based services | |||
| Context-dependent awareness support in open collaboration environments | | BIBAK | Full-Text | 223-254 | |
| Liliana Ardissono; Gianni Bosio | |||
| The widespread adoption of online services for performing work, home and
leisure tasks enables users to operate in the ubiquitous environment provided
by the Internet by managing a possibly high number of parallel (private and
shared) activity contexts. The provision of awareness information is a key
factor for keeping users up-to-date with what happens around them; e.g., with
the operations performed by their collaborators. However, the delivery of
notifications describing the occurred events can interrupt the users'
activities, with a possible disruptive effect on their emotional and
attentional states. As a possible solution to the trade-off between informing
and interrupting users, we defined two context-dependent notification
management policies which support the selection of the notifications to be
delivered on the basis of the user's current activities, at different
granularity levels: general collaboration context versus task carried out.
These policies are offered by the COntext depeNdent awaReness informAtion
Delivery (CONRAD) framework. We tested such policies with users by applying
them in a collaboration environment that includes a set of largely used Web 2.0
services. The experiments show that our policies reduce the levels of workload
on users while supporting an up-to-the-moment understanding of the interaction
with their shared contexts. The present paper presents the CONRAD framework and
the techniques underlying the proposed notification policies. Keywords: Personalized awareness information support; Notification management
policies; Interruption management; Collaboration environments; Context
awareness; Web 2.0 | |||
| Tune in to your emotions: a robust personalized affective music player | | BIBAK | Full-Text | 255-279 | |
| Joris H. Janssen; Egon L. van den Broek | |||
| The emotional power of music is exploited in a personalized affective music
player (AMP) that selects music for mood enhancement. A biosignal approach is
used to measure listeners' personal emotional reactions to their own music as
input for affective user models. Regression and kernel density estimation are
applied to model the physiological changes the music elicits. Using these
models, personalized music selections based on an affective goal state can be
made. The AMP was validated in real-world trials over the course of several
weeks. Results show that our models can cope with noisy situations and handle
large inter-individual differences in the music domain. The AMP augments music
listening where its techniques enable automated affect guidance. Our approach
provides valuable insights for affective computing and user modeling, for which
the AMP is a suitable carrier application. Keywords: Mood; Music; Psychophysiology; User modeling; Kernel density estimation;
Validation; Affective computing | |||
| Modeling sequences of user actions for statistical goal recognition | | BIBAK | Full-Text | 281-311 | |
| Marcelo G. Armentano; Analía A. Amandi | |||
| User goals are of major importance for an interface agent because they serve
as a context to define what the user's focus of attention is at a given moment.
The user's goals should be detected as soon as possible, after observing few
user actions, in order to provide the user with timely assistance. In this
article, we describe an approach for modeling and recognizing user goals from
observed sequences of user actions by using Variable Order Markov models
combined with an exponential moving average (EMA) on the prediction
probabilities. The validity of our approach has been tested using data
collected from real users in the Unix domain. The results obtained show that an
interface agent can achieve near 90% average accuracy and over 58% online
accuracy in predicting the most probable user goal after each observed action,
in a time linear to the number of goals being modeled. We also found that the
use of an EMA allows a faster convergence in the actual user goal. Keywords: Goal recognition; Variable Order Markov models; Interface agents; User
modeling | |||
| Preface to the special issue on user interfaces for recommender systems | | BIB | Full-Text | 313-316 | |
| Alexander Felfernig; Robin Burke; Pearl Pu | |||
| Evaluating recommender systems from the user's perspective: survey of the state of the art | | BIBAK | Full-Text | 317-355 | |
| Pearl Pu; Li Chen; Rong Hu | |||
| A recommender system is a Web technology that proactively suggests items of
interest to users based on their objective behavior or explicitly stated
preferences. Evaluations of recommender systems (RS) have traditionally focused
on the performance of algorithms. However, many researchers have recently
started investigating system effectiveness and evaluation criteria from users'
perspectives. In this paper, we survey the state of the art of user experience
research in RS by examining how researchers have evaluated design methods that
augment RS's ability to help users find the information or product that they
truly prefer, interact with ease with the system, and form trust with RS
through system transparency, control and privacy preserving mechanisms finally,
we examine how these system design features influence users' adoption of the
technology. We summarize existing work concerning three crucial interaction
activities between the user and the system: the initial preference elicitation
process, the preference refinement process, and the presentation of the
system's recommendation results. Additionally, we will also cover recent
evaluation frameworks that measure a recommender system's overall perceptive
qualities and how these qualities influence users' behavioral intentions. The
key results are summarized in a set of design guidelines that can provide
useful suggestions to scholars and practitioners concerning the design and
development of effective recommender systems. The survey also lays groundwork
for researchers to pursue future topics that have not been covered by existing
methods. Keywords: Research survey; Recommender systems; User experience research; Explanation
interface; Design guidelines | |||
| Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process | | BIBAK | Full-Text | 357-397 | |
| Alina Pommeranz; Joost Broekens | |||
| Two problems may arise when an intelligent (recommender) system elicits
users' preferences. First, there may be a mismatch between the quantitative
preference representations in most preference models and the users' mental
preference models. Giving exact numbers, e.g., such as "I like 30 days of
vacation 2.5 times better than 28 days" is difficult for people. Second, the
elicitation process can greatly influence the acquired model (e.g., people may
prefer different options based on whether a choice is represented as a loss or
gain). We explored these issues in three studies. In the first experiment we
presented users with different preference elicitation methods and found that
cognitively less demanding methods were perceived low in effort and high in
liking. However, for methods enabling users to be more expressive, the
perceived effort was not an indicator of how much the methods were liked. We
thus hypothesized that users are willing to spend more effort if the feedback
mechanism enables them to be more expressive. We examined this hypothesis in
two follow-up studies. In the second experiment, we explored the trade-off
between giving detailed preference feedback and effort. We found that
familiarity with and opinion about an item are important factors mediating this
trade-off. Additionally, affective feedback was preferred over a finer grained
one-dimensional rating scale for giving additional detail. In the third study,
we explored the influence of the interface on the elicitation process in a
participatory set-up. People considered it helpful to be able to explore the
link between their interests, preferences and the desirability of outcomes. We
also confirmed that people do not want to spend additional effort in cases
where it seemed unnecessary. Based on the findings, we propose four design
guidelines to foster interface design of preference elicitation from a user
view. Keywords: Preference elicitation; Constructive preferences; Interface design | |||
| Evaluating the effectiveness of explanations for recommender systems | | BIBAK | Full-Text | 399-439 | |
| Nava Tintarev; Judith Masthoff | |||
| When recommender systems present items, these can be accompanied by
explanatory information. Such explanations can serve seven aims: effectiveness,
satisfaction, transparency, scrutability, trust, persuasiveness, and
efficiency. These aims can be incompatible, so any evaluation needs to state
which aim is being investigated and use appropriate metrics. This paper focuses
particularly on effectiveness (helping users to make good decisions) and its
trade-off with satisfaction. It provides an overview of existing work on
evaluating effectiveness and the metrics used. It also highlights the
limitations of the existing effectiveness metrics, in particular the effects of
under- and overestimation and recommendation domain. In addition to this
methodological contribution, the paper presents four empirical studies in two
domains: movies and cameras. These studies investigate the impact of
personalizing simple feature-based explanations on effectiveness and
satisfaction. Both approximated and real effectiveness is investigated.
Contrary to expectation, personalization was detrimental to effectiveness,
though it may improve user satisfaction. The studies also highlighted the
importance of considering opt-out rates and the underlying rating distribution
when evaluating effectiveness. Keywords: Recommender systems; Metrics; Item descriptions; Explanations; Empirical
studies | |||
| Explaining the user experience of recommender systems | | BIBAK | Full-Text | 441-504 | |
| Bart P. Knijnenburg; Martijn C. Willemsen | |||
| Research on recommender systems typically focuses on the accuracy of
prediction algorithms. Because accuracy only partially constitutes the user
experience of a recommender system, this paper proposes a framework that takes
a user-centric approach to recommender system evaluation. The framework links
objective system aspects to objective user behavior through a series of
perceptual and evaluative constructs (called subjective system aspects and
experience, respectively). Furthermore, it incorporates the influence of
personal and situational characteristics on the user experience. This paper
reviews how current literature maps to the framework and identifies several
gaps in existing work. Consequently, the framework is validated with four field
trials and two controlled experiments and analyzed using Structural Equation
Modeling. The results of these studies show that subjective system aspects and
experience variables are invaluable in explaining why and how the user
experience of recommender systems comes about. In all studies we observe that
perceptions of recommendation quality and/or variety are important mediators in
predicting the effects of objective system aspects on the three components of
user experience: process (e.g. perceived effort, difficulty), system (e.g.
perceived system effectiveness) and outcome (e.g. choice satisfaction).
Furthermore, we find that these subjective aspects have strong and sometimes
interesting behavioral correlates (e.g. reduced browsing indicates higher
system effectiveness). They also show several tradeoffs between system aspects
and personal and situational characteristics (e.g. the amount of preference
feedback users provide is a tradeoff between perceived system usefulness and
privacy concerns). These results, as well as the validated framework itself,
provide a platform for future research on the user-centric evaluation of
recommender systems. Keywords: Recommender systems; Decision support systems; User experience; User-centric
evaluation; Decision-making; Human-computer interaction; User testing;
Preference elicitation; Privacy | |||