[Asis-l] Special Issue on "Data-Driven User Behavioral Modeling"

[Ludovico Boratto]

*Journal of Intelligent Information Systems (Springer)*
*Special Issue on “Data-Driven User Behavioral Modeling: From Real-World
Behavior to Knowledge, Algorithms, and Systems”*
http://data-driven.eurecat.org/


*MOTIVATION*
We are now *inundated* with user data – in the digital world and in the
real world – so it makes sense to try to mine that data to look for
patterns and rules to guide our recommendation algorithms. We capture data
streams from sensors, social media recommendations, mobile location-based
information, and the evolving Internet of Things (IoT). The goal is to
create a *snapshot*, or profile, of the user by understanding a person’s
behavior when searching for a product, user activities when near a store
that has a previously search-for product, and how social recommendations
may influence a decision. The data tells the much of the user’s story, but
we need tools and techniques to look for patterns, and turn those patterns
into knowledge that can guide our algorithms in making smarter
recommendations.

Data is being collected constantly on user behavior on the Web, by
location-based services using mobile phones, tele-monitoring and home
support systems, and on our mobile fitness apps, and by sensors, cameras,
and the IoT. Our goal is to *transform* that data into knowledge in ways
that support and enhance the user experience. We want to make recommender
systems smarter and more responsive to user needs, so we need to understand
our users better. One important requirement is that users be able to
provide *feedback* regarding the recommendations provided by the system.
Another important factor is the role of social media in the way users are
influenced in their decision-making.


*TOPICS FOR THE SPECIAL ISSUE*
We are interested in original research that addresses the multitude of
issues in Data-Driven User Behavior Modeling. Topics include, but are not
limited to the following:

   - Data mining of user behavior from data streams;
   - Knowledge discovery for user behavior modeling;
   - Internet of Things and daily activity monitoring;
   - Recommender systems for user decision-making;
   - Algorithms that incorporate user behavior models;
   - Role of social media and recommendations for user decision-making;
   - Real-world applications and systems in healthcare and other areas;
   - User behavior modeling and data privacy and data security.


*IMPORTANT DATES*

   - First submission paper due: October 1, 2017
   - First round decision made: December 15, 2017
   - Revised manuscript due: January 31, 2018
   - Final decision made: March 15, 2018
   - Final paper due: April 15, 2018


*SUBMISSION GUIDELINES*
Paper submissions must conform to the Journal of Intelligent Information
Systems format guidelines
<http://www.springer.com/computer/database+management+%26+information+retrieval/journal/10844>
.

Manuscripts should be around (but not longer than) 25 pages and must be
submitted to the online submission system
<http://www.editorialmanager.com/jiis/>. Please, select option "Data-Driven
User Behavioral Modeling: From Real-World Behavior to Knowledge,
Algorithms, and Systems" in the "Choose Article Type" section.

Submissions to this Special Issue must represent original material that has
been neither submitted to, nor published in, any other journal. A
submission based on one or more papers that appeared elsewhere should have
at least 30% of novel valuable content that extends the original work (the
original papers should be referenced and the novel contributions should be
clearly stated in the submitted paper).


*CONTACTS*
Website: http://data-driven.eurecat.org/
For enquiries regarding the special issue, send an email to both guest
editors at ludovico.boratto at acm.org and eloisa.vargiu at eurecat.org.


*GUEST EDITORS*
Ludovico Boratto - Digital Humanities unit, EURECAT (Spain)
Eloisa Vargiu - eHealth unit, EURECAT (Spain)