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In this paper we present a first version of a recommender system in the domain of cultural heritage based on semantic web technology. Our recommender system gives advice on categories and topics of interest, based on the users' judgement, indicating like or dislike, of a small sample of artefacts from the collection of the museum.

A first user study indicates that our recommender system actually helps novice users, having little knowledge of the history of art or the collection of artefacts in the museum, to select categories and topics of interest from the otherwise confusing catalogue of the museums collection.

Based on the apparently promising results of our user study, which included the assessment of a personal profile, as well as an evaluation of the usability of our prototype, we will further explore the development of a personalized recommender system based on semantic web technology, following well-established methods of user-centered design.


In the last years, dedicated recommender systems have gained popularity, and became more and more established practice in online commerce, for example for the purchase of books and DVDs.

In our project, CHIP, we aim to develop recommender systems in a more generic way, based on semantic web technology, to allow for the disclosure of rich collections of information in the public and cultural domain, aiding the user in navigation and interaction.

In this paper, we present a user study on a first prototype embodying our approach, to gain insight in the way users understand the purpose of such systems and how they may profit from them. The case study concerns a recommender system that proposes categories and topics of interest based on a preference rating of a small sample of artefacts from a museum collection, in our case the Rijksmuseum, Amsterdam.

The actual user study consists of four questionaires and one trial with our system, resulting in a list of potentially interesting categories and topics. Two questionaires asked the user explicitly to indicate his/her interest in particular categories and topics, one taken before and the other after the trial. In addition, one questionaire was meant to determine the level of expertise of the user, that is his/her familiarity with art, the history of art, and the collection of the museum, and one questionaire, taken at the end of the session, was used to evaluate the overall usability of our system.

In this way we wished to obtain feedback on our approach, that is more specifically, whether we could realize a recommender system in the domain of cultural heritage, using generic semantic web technology, to give access to the collection of artefacts of the museum. As such, if our assumptions hold, our prototype may be considered a showcase for a new approach to recommender systems, using emerging semantic web standards for interesting ways of feedback and user guidance. Our actual system is meant to be the first in a sequence, to be followed by a system that generates personalized guided tours, based on a user profile generated from a preference rating of a small sample of artefacts or examples.

In order to obtain real information we decided to perform the user study with real visitors from the Rijksmuseum, selected randomly at the entrance of the museum, instead of a potentially more clean study in an academic environment, using students as participants.

Our working hypothesis in this study was that novice users would profit from our recommender system, whereas for expert users, given the simplicity of this prototype, it would not have much to offer. To test this hypothesis, we compared the results of the questionaire taken before the trial with the results of the questionaire about categories and topics taken after the trial, or more precisely, we compared the respective relationships between the outcome of the trial and the results of the questionaires.

Our results look promising, confirming the trend we hoped for, in particular when we define level of expertise on a sufficiently wide range from absolute novice to established expert.

Equally important, though, are perhaps the findings we obtained from the usability evaluation questionaire, which gives clear indications what factors do influence the acceptance of the recommender system, and what aspects may confuse the user. These insights will govern our subsequent design of recommender systems, which will offer more functionality, following well-established practices of user-centered design.


The structure of this paper is as follows ...

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(C) Æliens 2014