Welcome!

We are pleased to announce the second workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE), held in conjunction with the 23rd ACM International Conference on Intelligent User Interfaces (IUI) 2018 in Tokyo, Japan.

 

What is the workshop about?

When designing interfaces practitioners often rely on knowledge and experience about the interface’s intended users and their needs in order to provide the optimal interface for its users. When creating user interfaces that can be personalized, quite often a more data-driven approach is taken, where practitioners rely on methods that use implicit or explicit feedback to prescribe how to alter an interface.

The current workshop aims at soliciting work that investigates the potential of combining the more practical data mining/machine learning methods with a more theory-driven approach. Three aspects play an important role in taking a more theory-driven approach to personalization:

  1. How to consider the users of a system and their individual differences.
  2. How to infer these individual differences from interaction data.
  3. How to translate individual differences into interface designs.

The characteristics that play a role in what a user needs or wants from a system need to be investigated. Knowing what users differ on allows us to alter the interface. These characteristics 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 approaches exist for this more computational challenge, with for example mining data from social media and clickstream analysis.

A third aspect is knowing how to adapt an interface to match a certain type of user. When a user’s characteristics are known, the interface can be altered to match this user. For example by reducing the number of search results for users under high cognitive load, or manipulating diversity.

These challenges are interconnected and there is no natural order in which these aspects need to be addressed when personalizing an interface. For example, by analyzing behavior data we can identify potential individual characteristics that play a role in people’s needs.

HUMANIZE provides scholars and practitioners in the field of personalized user interfaces with a venue to discuss and explore the commonalities between the sub-problems involved with user interface personalization.

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