Journal of Intelligent Information Systems (Springer)
Special Issue on “Data-Driven User Behavioral Modeling: From Real-World Behavior to Knowledge, Algorithms, and Systems”

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 for users to 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: November 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@acm.org and eloisa.vargiu@eurecat.org.


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