Abstract
Advances in mobile computing and user modelling have enabled technologies that help museum visitors select personally interesting exhibits to view. This is done by generating personalised exhibit recommendations on the basis of non-intrusive observations of visitors’ behaviour in the physical museum space. We describe a simple methodology for manually annotating museum exhibits with bags of keywords (viewed as item features), and present two personalised keyword-based models for predicting a visitor’s viewing times of unseen exhibits from his/her viewing times at visited exhibits (viewing time is indicative of interest). Our models were evaluated with a real-world dataset of visitor pathways collected by tracking visitors in a museum. Both models achieve a higher predictive accuracy than a non-personalised baseline, and perform at least as well as a nearest-neighbour collaborative filter.
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Bohnert, F., Zukerman, I. (2009). Using Keyword-Based Approaches to Adaptively Predict Interest in Museum Exhibits. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_66
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DOI: https://doi.org/10.1007/978-3-642-10439-8_66
Publisher Name: Springer, Berlin, Heidelberg
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