Rate, Recommend, Regret -- an Expert-based Approach to the Personalization of Guided Tours

Anton Eliëns, Yiwen Wang

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abstract

In this paper we propose an approach to generate personalized guided tours based on a finite collection of tours obtained by tracking the navigation of expert users. Our proposal is based on a variant of decision theory, that uses a regret function to measure the difference between a proposed decision and a finite collection of expert decisions, generalized to a finite sequence of discrete choices. Personalization may then be seen as a minimization problem over a weighting scheme, expressing the relative importance of experts of which tours are available. We illustrate our approach by showing how we may obtain guided tours in 3D digital dossiers containing information on contemporary art installations, and discuss how our approach may be applied in other cultural heritage applications.
keywords: decision theory, personalization, guided tours, digital dossier, cultural heritage

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