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This paper describes how the combination of recommendation and Semantic Web techniques can enhance user interaction with digital cultural heritage collections. The interface that this paper describes adapts user interaction to individual interest in both artworks and abstract concepts related to them. This paper presents an overview of several studies that evaluate a recommender system for museum collections. The findings and requirements derived from them form a basic framework for personalized exhibition tours that present multiple cultural artifacts as a structured and cohesive whole.

User-adaptive information filters can be a tool to achieve timely delivery of the right information to the right person, a feat critical in crisis management. This paper explores interaction issues that need to be taken into account when designing a user-adaptive information filter. Two case studies are used to illustrate which factors affect trust and acceptance in user-adaptive filters as a starting point for further research. The first study deals with user interaction with user-adaptive spam filters. The second study explores the user experience of an art recommender system, focusing on transparency. It appears that while participants appreciate filter functionality, they do not accept fully automated filtering. Transparency appears to be a promising way to increase trust and acceptance, but its successful implementation is challenging. Additional observations indicate that careful design of training mechanisms and the interface will be crucial in successful filter implementation.

This paper describes ongoing work exploring aspects of personalized access to and presentation of virtual museum collections. The project demonstrator illustrates an interactive approach to collecting data about museum visitors in terms of their interests in and preferences about artefacts from the Rijksmuseum collection. This data is stored in user profiles used further to recommend routes through the museum and to guide the users towards artefacts related to their interests and preferences. The overall goal of the project is to explore different users' characteristics and personalize users' museum experiences within the Rijksmuseum virtual and physical collections.

We have developed a set of small interactive throw pillows containing intelligent touch-sensing surfaces, in order to explore new ways to model the environment, participants, artefacts, and their interactions, in the context of expressive non-verbal interaction. We present the overall architecture of the environment, describing a model of the user, the interface (the interactive pillows and the devices it can interact with) and the context engine. We describe the representation and process modules of the context engine and demonstrate how they support real-time adaptation. We present an evaluation of the current prototype and conclude with plans for future work.

Autocompletion is a commonly used interface feature in diverse applications. Semantic Web data has, on the one hand, the potential to provide new functionality by exploiting the semantics in the data used for generating autocompletion suggestions. Semantic Web applications, on the other hand, typically pose extra requirements on the semantic properties of the suggestions given. When the number of syntactic matches becomes too large, some means of selecting a semantically meaningful subset of suggestions to be presented to the user is needed. In this paper we identify a number of key design dimensions of autocompletion interface components. Our hypothesis is that a one-size-fits-all solution to autocompletion interface components does not exist, because different tasks and different data sets require interfaces corresponding to different points in our design space. We present a fully configurable architecture, which can be used to configure autocompletion components to the desired point in this design space. The architecture has been implemented as an open source software component that can be plugged into a variety of applications. We report on the results of a user evaluation that confirms this hypothesis, and describe the need to evaluate semantic autocompletion in a task and application-specific context.