Abstract
Visitors to cultural heritage sites are often overwhelmed by the information available in the space they are exploring. The challenge is to find items of relevance in the limited time available. Mobile computer systems can provide guidance and point to relevant information by identifying and recommending content that matches a user’s interests. In this paper we infer implicit ratings from observed viewing times, and outline a collaborative user modelling approach to predict a user’s interests and expected viewing times. We make predictions about viewing themes (item sets) taking into account the visitor’s time limit. Our model based on relative interests with imputed ratings yielded the best performance.
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Bohnert, F., Zukerman, I. (2007). Using Viewing Time for Theme Prediction in Cultural Heritage Spaces. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_38
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DOI: https://doi.org/10.1007/978-3-540-76928-6_38
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