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We are pleased to announce the first workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE), held in conjunction with the 22nd ACM  International Conference on Intelligent User Interfaces (IUI) 2017 in Limassol, Cyprus.

What is the workshop about?

When designing (user) interfaces, practitioners rely on knowledge and experience about the interfaces’ intended users and their needs (in the form of a theory-informed model, such as the user’s cognitive style or personality) in order to provide the optimal interface for its users. In contrast to this, in content recommendations, a more data-driven approach is taken without the need to have to rely on user knowledge.

Combining these two approaches, model-driven and data-driven,  provides an interesting research direction. By relying on knowledge about what types of users require what type of interface, and by using data mining techniques to infer the types of users from interaction behavior, interfaces can be personalized in an informed, grounded way.

Advances in combining formal user models with data mining can be made in grossly two ways that complement each other. On the one hand this can be done by identifying formal user models that can be used to base personalization on (such as cognitive style). On the other hand this can be done by finding ways to infer these user models from data. These two ways complement each other and can be combined in a theory-informed personalization.

The HUMANIZE workshop combines practical data mining methods and theoretical knowledge for personalization, so it provides a venue where researchers from different fields come together to share their thoughts and experiences. In addition, the workshop will allow for an exploration of future opportunities in hopes of identifying possible links between the algorithmic side of behavioral analysis and the theoretical understanding of users for personalization.

A non-exhaustive list of topics for this workshop:

  • Psychological theory that can be used for personalization (e.g., personality, level of domain knowledge, need for cognition, cognitive styles)
  • Data mining methods to infer user profiles in terms of psychological models (e.g., how to infer someone’s personality from their social media)
  • How (user) interfaces can be tailored to better match certain user profiles (e.g., altering the number of search results, ordering of interface elements, visual versus textual representations)
  • User studies investigating one or more of the above points

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