The vast amount of information presented in museums is often overwhelming to a visitor, making it difficult to select personally interesting exhibits. Advances in mobile computing and user modelling have made possible technology that can assist a visitor in this selection process. Such a technology can (1) utilise non-intrusive observations of a visitor’s behaviour in the physical space to learn a model of his/her interests, and (2) generate personalised exhibit recommendations based on interest predictions. Due to the physicality of the domain, datasets of visitors’ behaviour (i.e. visitor pathways) are difficult to obtain prior to deploying mobile technology in a museum. However, they are necessary to assess different modelling techniques. This paper reports on a methodology that we used to conduct a manual data collection, and describes the dataset we obtained. We also present two collaborative 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), and evaluate our models with the dataset we collected. Both models achieve a higher predictive accuracy than a non-personalised baseline.