- Innovation strategy - Service and business designer - Behaviour design - Training design thinking- professional speaker

Teaching:- Consumer psychology- Perceptieleer- Psychology and perception 1- Psychology and perception 2- Game psychology- Geschiedenis van de actua en nieuwe media 1- Interaction psychology

User Experience DesignService DesignSpeaker

Modelling user preferences in multi-media recommender systemsSummaryToday we are in the forefront of a transformation of how we are going use the web. In the last two decades we witnessed the transformation from a one-to-many static web in which an author determined the content of a webpage (web1.0) to a many-to-many dynamic web where every user is also a content producer (web2.0). Together with the rapid evolution in web technology, increasing computation and storage capacity this quickly led to an exponential growth in digital content. This ever increasing digital content, welcome as it may be, has also led to some challenges in that it is not evident for an individual to search through large amounts of information only to find the content he is looking for. Although the immense amount of ever-growing digital content provides the opportunity for a person to find content that fits his personal needs; it also causes an information overload since there are many good alternatives available to choose from. Currently we are moving towards a personalized web experience. In contrast to an explicit action of a user to search content on the web, the personalised web delivers information to the user when needed. At the core of this personalized web are intelligent computing methods, such as recommender systems, that enable to assess individuals’ needs, goals and preferences and match these with content on the web. Providing good recommendations at least entails the assessment of preference and the prediction of preferred content bases on previously collected preferences. In this dissertation I present an interaction model of constructive preference which takes the close interaction between user characteristics and recommender system together with contextual properties into account.