3rd CfP: Special Issue on Harnessing personal tracking data for personalization and sense-making at UMUAI: User Modeling and User-Adapted Interaction Journal

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Call For Papers
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Special Issue on Harnessing personal tracking data for personalization and
sense-making

at

User Modeling and User-Adapted Interaction. The Journal of Personalization
Research (UMUAI)

IF 2016: 3.625

First deadline: 15th November 2017: Submission of title and abstract

http://www.umuai.org/news_on_journal.html

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Scope
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In recent years, growing numbers of people are tracking a variety of
personal data via diverse tools, using devices ranging from desktops to
smartphones, from ubiquitous system devices to wearable devices. Examples
include keeping records of social interactions, emails, and social media
status updates, physiological and emotional status, or activities such as
viewing television, use of time in general, driving habits, work
productivity, monitoring environmental conditions, and so on. This
phenomenon of self-tracking has had a vanguard group of early adopters, the
so-called ìQuantified Selfî (QS) movement, an Internet community focusing
on self-quantification through technological aids. However, with the
growing availability of personal data trackers, this phenomenon is now
spreading to a far wider audience than the QS community. The number of
tracking devices reached 51 million units in 2015 and various reports
suggest that
they will reach 220 million units by 2020. This makes it timely to tackle
the core challenges that people face in making effective use of their
personal tracking data.

This special issue brings together research that aims to transform tracking
data into user models that can support personalization of software and open
user model interfaces that enable individuals self-reflect, self-monitor
and plan how to achieve their long term goals. On the one hand, user models
can now be expanded to make use of a variety of information concerning the
userís attitudes, emotions, tastes, physiology, movements, everyday
behaviours, habits, working and learning performances, media uses, and
preferences. Such information may create rich ìQS user modelsî, i.e. users
models nurtured by personal tracking data. In principle, these could model
diverse aspects of the userís real and digital life and be turned into
life-long and holistic digital mirrors. However, some important research
questions arise:

- How can we merge these heterogeneous data to obtain a comprehensive,
semantic, and dynamic representation of the diverse aspects regarding
users?
- How can we create reasoning tools for such data to create meaningful QS
user models that can drive personalization?
- And can we enable predictions about the usersí behaviour, health, and
objectives?
- How can we mine such data to detect and model trends in time and
unexpected correlations among different aspects of their life?
- How can all of these be done in ways that match individualís privacy
preferences.

On the other hand, modeling all this information in a comprehensive
representation of the user may give life to new forms of personalization
embedded in daily life, such as real-world, context-aware, just-in-time
recommendations and services, tailored on the usersí changing state, goals,
and environments. Here further research questions appear to be fundamental:

- How can we deal with different, and even conflicting, data sources to
create user models to drive recommendations?
- How can we exploit information coming from self-tracking in personalized
systems?
- How can we adapt applications accordingly to the continuous flux of data
regarding diverse aspects of the user?
- How can we create user models that address peopleís privacy concerns and
ensure people can understand the ways their user model was created and may
be used?

Finally, when these enriched QS user models have suitable interfaces, they
become a form of lifelong and life-wide Open Learner Model (OLM)
facilitating metacognitive processes of self-reflection, self-monitoring
and planning, based on long term user models.

- How can we present a comprehensive model of the user, creating an
effective interface and supporting interaction with it?
- How can we represent relations, correlations, and trends among the
different aspects that make up the model?

Such use of personal data to create user models creates three key
challenges. First, it tackles the gathering of personal information with
emerging devices that make this increasingly easy to do for many spheres of
life. Secondly, it deals with the multiplicity of challenges for
transforming that data into user models that people can control and that
address challenges they face. The third challenge concerns sharing partial
data collections of certain aspects of oneís life with a worldwide
audience, which can be used to create aggregate user models.

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Relevant Topics
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With so much potential still to explore, a special issue on the topic is
aimed to both focus on research in this new and exploding area as well as
to provide a key milestone in the state of the art. Relevant topics include
but are not limited to:

- Techniques to harness real world data, such as physiological data,
behaviours, habits, emotional states, and incorporate these in ìQS user
modelsî that provide useful services;
- Techniques to create, manage, visualize, and interact with QS user models
spanning long periods of time, and including a large variety of data;
- Techniques to make such user models interoperable;
- New personalized applications that exploit user models enriched with data
coming from QS tools;
- New personalized services (e.g. in health, learning, cultural heritage
domain) capable of adapting themselves to the continuous data streams
coming from personal trackers;
- New recommender systems based on personal data and their impacts on users;
- Evaluations of QS user models and personalized systems that leverage
personal data;
- Ethical challenges and theoretical reflections on how personalization
could change in the future thanks to the availability of new data sources.


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Timeline
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- 15th November 2017: Submission of title and abstract
- 1st December 2017: Notification of suitability of abstract
- 1st May 2018: Submission of full papers
- 1st August 2018: First round of review notifications
- 1st October 2018: Revisions of papers due
- 1st December 2018: Final notifications due
- 15th December 2018: Camera ready papers due


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Guest Editors
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- Frank Hopfgartner, University of Glasgow, UK.
- Judy Kay, University of Sydney, Australia
- Amon Rapp, University of Torino, Italy

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Submission Details
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Abstract should be submitted via easychair to: https://easychair.org/conf
erences/?conf=umuaisiqs2018
Submission instructions for full papers are available at the journal's
website: http://www.umuai.org/paper_submission.html

For any inquiry please write to: amon.rapp@gmail.com