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https://ceur-ws.org/Vol-2068/preface-humanize.pdf
Second Workshop on Theory-Informed User Modeling for
Tailoring and Personalizing Interfaces (HUMANIZE):
Workshop Preface
Mark Graus Bruce Ferwerda Marko Tkalcic
Human-Technology Department of Computer Faculty of Computer Science
Interaction Group Science and Informatics Free University of
Eindhoven University of School of Engineering Bozen-Bolzano
Technology Jönköping University Piazza Domenicani 3
P.O. Box 513 P.O. Box 1026 I-39100, Bozen-Bolzano, IT
5600 MB, Eindhoven, NL SE-551 11, Jönköping, SE marko.tkalcic@unibz.it
m.p.graus@tue.nl bruce.ferwerda@ju.se
Panagiotis Germanakos∗
UX, Mobile & Business
Services, P&I ICD
SAP SE
Dietmar-Hopp-Allee 16
69190, Walldorf, DE
panagiotis.germanakos@sap.com
ABSTRACT their needs in order to provide the optimal interface for its
The second workshop on Theory-Informed User Modeling for users. When creating user interfaces that can be personalized,
Tailoring and Personalizing Interfaces (HUMANIZE)1 took quite often a more data-driven approach is taken, where practi-
place in conjunction with the 23rd annual meeting of the in- tioners rely on methods that use implicit or explicit feedback
telligent user interfaces (IUI)2 community in Tokyo, Japan to prescribe how to alter an interface.
on March 11, 2018. The goal of the workshop was to attract
The current workshop aims at soliciting work that investigates
researchers from different fields by accepting contributions
the potential of combining the more practical data mining/ma-
on the intersection of practical data mining methods and theo-
chine learning methods with a more theory-driven approach.
retical knowledge for personalization. A total of eight papers
Three aspects play an important role in taking a more theory-
were accepted for this edition of the workshop.
driven approach to personalization:
Author Keywords
1. How to consider the users of a system and their individual
User modeling, personalization, tailoring, user interfaces differences.
2. How to infer these individual differences from interaction
INTRODUCTION data.
When designing interfaces practitioners often rely on knowl- 3. How to translate individual differences into interface de-
edge and experience about the interface’s intended users and signs.
* Also affiliated with the Department of Computer Science, Uni-
versity of Cyprus, P.O. Box 20537, 1678, Nicosia, Cyprus, pger- The characteristics that play a role in what a user needs or
man@cs.ucy.ac.cy wants from a system need to be investigated. Knowing what
1 https://humanize2018.wordpress.com/
users differ on allows us to alter the interface. These charac-
2 http://iui.acm.org/2018/
teristics can then be used to construct a user model containing
this information. Examples of characteristics that may play a
role in how to design an optimal interface are cognitive style,
personality, and susceptibility to persuasive strategies.
Secondly, there is a challenge of profiling a user in terms
of these characteristic based on interaction data. Several ap-
proaches exist for this more computational challenge, for ex-
©2018. Copyright for the individual papers remains with the authors. ample mining data from social media and clickstream analysis.
Copying permitted for private and academic purposes.
HUMANIZE ’18, March 11, 2018, Tokyo, Japan
A third aspect is knowing how to adapt an interface to match a REFERENCES
certain type of user. When a user’s characteristics are known, 1. Bruce Ferwerda and Marko Tkalcic. 2018. You Are What
the interface can be altered to match this user. For example You Post: What the Content of Instagram Pictures Tells
by reducing the number of search results for users under high About Users’ Personality. In Companion Proceedings of
cognitive load, or manipulating diversity. the 23rd International on Intelligent User Interfaces: 2nd
Workshop on Theory-Informed User Modeling for
These challenges are interconnected and there is no natural Tailoring and Personalizing Interfaces (HUMANIZE).
order in which these aspects need to be addressed when per-
sonalizing an interface. For example, by analyzing behavior 2. Mark Graus, Martijn Willemsen, and Chris Snijders.
data we can identify potential individual characteristics that 2018. Personalizing a parenting app: parenting-style
play a role in people’s needs. surveys beat behavioral reading-based models. In
Companion Proceedings of the 23rd International on
HUMANIZE provides scholars and practitioners in the field Intelligent User Interfaces: 2nd Workshop on
of personalized user interfaces with a venue to discuss and Theory-Informed User Modeling for Tailoring and
explore the commonalities between the sub-problems involved Personalizing Interfaces (HUMANIZE).
with user interface personalization.
3. Maciej J Korzepa, Benjamin Johansen, Michael K
An non-exhaustive list of topics for this workshop: Petersen, Jan Larsen, Jakob E Larsen, and Niels H
• Identifying models that are (expected to be) useful for per- Pontoppidan. 2018. Learning preferences and
sonalizing user interfaces (e.g., personality, level of domain soundscapes for augmented hearing. In Companion
knowledge, need for cognition, cognitive styles) Proceedings of the 23rd International on Intelligent User
• Data mining methods to infer user profiles in terms of cogni- Interfaces: 2nd Workshop on Theory-Informed User
tive/psychological user characteristics from data (e.g., how Modeling for Tailoring and Personalizing Interfaces
to infer personality from social media or domain knowledge (HUMANIZE).
from clickstreams) 4. Johannes Kunkel, Tim Donkers, Catalin-Mihai Barbu,
• Theory on how to tailor interfaces to better match certain and Jurgen Ziegler. 2018. Trust-related Effects of
user profiles (e.g., altering the number of search results, Expertise and Similarity Cues in Human-Generated
ordering of interface elements, visual versus textual repre- Recommendations. In Companion Proceedings of the
sentations) 23rd International on Intelligent User Interfaces: 2nd
• User studies investigating one or more of the aspects men- Workshop on Theory-Informed User Modeling for
tioned above Tailoring and Personalizing Interfaces (HUMANIZE).
CONTRIBUTIONS 5. Alixe Lay and Bruce Ferwerda. 2018. Predicting users’
A total of eight papers were accepted: 3 long papers, 4 short personality based on their ’liked’ images on Instagram. In
papers, and 1 position paper. Papers were categorized into one Companion Proceedings of the 23rd International on
of the three sessions: 1) Personality, 2) Social, or 3) Health & Intelligent User Interfaces: 2nd Workshop on
Wellbeing. Below a description of the accepted papers: Theory-Informed User Modeling for Tailoring and
Personalizing Interfaces (HUMANIZE).
Personality. Lay and Ferwerda [5] proposes a new view
on how to incorporate meta data of Instagram users to infer 6. Yu Xu and Michael J Lee. 2018. Shopping as a Social
their personality traits. Similarly, Ferwerda and Tkalcic [1] Activity: Understanding People’s Categorical Item
analyzed the content of Instagram pictures and found distinct Sharing Preferences on Social Networks. In Companion
correlations with personality traits. Zheng [7] on the other Proceedings of the 23rd International on Intelligent User
hand investigated how personality traits influence individual Interfaces: 2nd Workshop on Theory-Informed User
and group decision making. Modeling for Tailoring and Personalizing Interfaces
(HUMANIZE).
Social. Xu and Lee [6] explored what kind of products people
choose to share on their social networks that they have bought 7. Yong Zheng. 2018. Identifying Dominators and
online. Kunkel et al. [4] compared the effect of personal Followers in Group Decision Making Based on The
and impersonal recommendation sources, and investigated the Personality Traits. In Companion Proceedings of the 23rd
influence of traits of personal recommendation sources on a International on Intelligent User Interfaces: 2nd
user’s trust in recommendations Workshop on Theory-Informed User Modeling for
Tailoring and Personalizing Interfaces (HUMANIZE).
Health & Wellbeing Korzepa et al. [3] describes how to use
behavioral data for personalized hearing aid profiles. Zhou et 8. Mo Zhou, Yonatan Mintz, Yoshimi Fukuoka, Ken
al. [8] are using reinforcement learning to generate personal- Goldberg, Elena Flowers, Philip Kaminsky, Alejandro
ized motivators for fitness applications that are challenging but Castillejo, and Anil Aswani. 2018. Personalizing Mobile
attainable. Graus et al. [2] showed that personalization based Fitness Apps using Reinforcement Learning. In
on parenting styles gained a higher perceived personalization Companion Proceedings of the 23rd International on
and satisfaction than reading-based personalization. Intelligent User Interfaces: 2nd Workshop on
Theory-Informed User Modeling for Tailoring and
Personalizing Interfaces (HUMANIZE).