Department of Computational Perception
Department of
Computational Perception
Johannes Kepler Universität Linz

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UMUAI - Special Issue on Personality in Personalized Systems

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

Extended abstract submission deadline: December 1, 2014

Paper submission deadline (for accepted abstracts): March 1, 2015

Special Issue Web site:

UMUAI Web site:

Flyer (PDF)

Scope of the special issue

Personality has been found to correlate with a number of real-world behaviors. For example, it correlates with musical taste: popular music tends to be liked by extroverts, whereas people with a tendency to be less open to experience tend to prefer religious music and to dislike rock music. Personality also impacts on the forming of social relations: friends tend to be, to a very similar extent, open to experience and extrovert. Furthermore, there is a strong correlation between personality and how people prefer to learn, indicating that learning styles can be seen as a subset of personality. Since personality has been shown to affect real-world user preferences (e.g. preferences for interaction styles, preferences for learning, preferences for musical genres), we might conclude that the design of online services (e.g., personalized user interfaces, music recommender systems, adaptive educational systems, and games) might also benefit from personality studies.

This is the reason why researchers have recently explored the extent to which personality traits impact on the use of interactive and hypermedia systems. They found, for example, that personality is associated with specific preferences for music genres online, and that this greatly impacts on music-information retrieval services. Collaborative filtering techniques have also benefited from assessing the users’ personality traits. It has also been shown that users open to new experiences (one of the big five personality traits) tend to prefer more diverse and serendipitous items (e.g., movies). Furthermore, learning styles have been heavily used in educational systems to personalize courses in terms of the structure and presentation of learning materials. In the context of games, for example, it has been found that personality seems to impact on the motivation for playing online games. Also, certain personality traits have been found to correlate with communication styles and, as a consequence, the adoption of location-sharing social media.

The five-factor model of personality, or the Big Five, is the most commonly used set of personality concepts and one of the most reliable and comprehensive models of personality. In this model, an individual is associated with five scores that correspond to the five main personality traits. The names of those traits form the acronym OCEAN: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism.

Other models of personality are, for example, the Four Temperaments (the oldest general model), the Benziger brain type (a work-related model), the Belbin team roles model, the Myers-Briggs types (general and team-working model), the RIASEC vocational model or the Bartle types (describing personalities in video games).

While personality traits are normally identified by asking people to complete a questionnaire, researchers have recently shown that personality traits can be extracted implicitly from the users' streams (e.g., tweets, Facebook updates) without resorting to time-consuming questionnaires. Furthermore, players’ behaviors in games have been investigated and can also provide information about a player’s personality. Similarly, several researchers have conducted studies on using data from learners’ behaviors in a course to automatically identify their learning styles.


The topics of interest for this special issue include (but are not limited to):

Paper submission and reviewing process

The prospective authors must first submit an extended abstract of no more than 4 single-spaced pages, formatted with 12-pt font and 1-inch margins, through easychair:

by December 1, 2014. This abstract should be preceded by a completed UMUAI self-assessment form that can be found at, preferably both in a single PDF file.

All submitted abstracts will receive an initial screening by the editors of the special issue. The authors of the abstracts will be notified about the results of the initial screening by December 15, 2014 . Abstracts that do not pass this initial screening (i.e., the abstracts that are deemed not to have a reasonable chance of acceptance) will not be considered further.

Authors of abstracts that pass the initial screening will be invited to submit the full version of the paper by March 1, 2015. The formatting guidelines and submission instructions for full papers can be found at Papers should not exceed 40 pages in journal format. Each paper submission should note that it is intended for the Special Issue on Personality in Personalized Systems and be submitted via email to the address mentioned in the submission instructions given above (

The tentative timeline for the special issue is as follows:


Marko Tkalčič, Johannes Kepler University, Linz, Austria

Daniele Quercia, Yahoo Labs, Barcelona, Spain

Sabine Graf, Athabasca University, Edmonton, Canada

last edited by mt on Feb 17, 2014