The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study's results.

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Abstract. In this paper we present an approach for personalized access to museum collections. We use a RDF/OWL specification of the Rijksmuseum Amsterdam collections as a driver for an interactive dialog. The user gives his/her judgment on the artefacts, indicating likes or dislikes. The elicited user model is further used for generating recommendations of artefacts and topics. In this way we support exploration and discovery of information in museum collections. A user study provided insights in characteristics of our target user group, and showed how novice and expert users employ their background knowledge and implicit interest in order to elicit their art preference in the museum collections.

Natalia Stash received her PhD from the Eindhoven University of Technology (TU/e), The Netherlands. The title of her thesis is "Incorporating Cognitive/Learning Styles in a General-Purpose Adaptive Hypermedia System". She currently participates in the CHIP project (Cultural Heritage Information Personalization) located at the Rijksmuseum in Amsterdam. Her research interests include adaptive web-based systems, semantic web technologies, learning styles, e-culture applications.

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a b s t r a c tThis article presents the CHIP demonstrator 1 for providing personalized access to digital museum collections. It consists of three main components: Art Recommender, Tour Wizard, and Mobile Tour Guide. Based on the semantically enriched Rijksmuseum Amsterdam 2 collection, we show how Semantic Web technologies can be deployed to (partially) solve three important challenges for recommender systems applied in an open Web context: (1) to deal with the complexity of various types of relationships for recommendation inferencing, where we take a content-based approach to recommend both artworks and art-history topics; (2) to cope with the typical user modeling problems, such as cold-start for first-time users, sparsity in terms of user ratings, and the efficiency of user feedback collection; and (3) to support the presentation of recommendations by combining different views like a historical timeline, museum map and faceted browser. Following a user-centered design cycle, we have performed two evaluations with users to test the effectiveness of the recommendation strategy and to compare the different ways for building an optimal user profile for efficient recommendations. The CHIP demonstrator received the Semantic Web Challenge Award (third prize) in 2007, Busan, Korea.

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