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Personalised viewing-time prediction in museums

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Abstract

People are often overwhelmed by the large amount of information provided in museum spaces, which makes it difficult for them to select exhibits of potential interest. As a first step in recommending exhibits where a visitor may wish to spend some time, this article investigates predictive user models for personalised prediction of museum visitors’ viewing times at exhibits. We consider two content-based models and a nearest-neighbour collaborative filter, and develop a collaborative model based on the theory of spatial processes which relies on a notion of distance between exhibits. We discuss models of exhibit distance derived from viewing-time similarity, semantic similarity and walking distance. The results from our evaluation with a real-world dataset of visitor pathways collected at Melbourne Museum (Melbourne, Australia) suggest that utilising walking and semantic distances between exhibits enables more accurate predictions of a visitor’s viewing times of unseen exhibits than using distances derived from observed exhibit viewing times. Our results also show that all models outperform a non-personalised baseline, that content-based viewing time prediction yields better results than nearest-neighbour collaborative prediction, and that our collaborative model based on spatial processes attains the highest predictive accuracy overall.

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Notes

  1. Memory-based and model-based methods have traditionally only been distinguished for collaborative approaches, but this classification may also be adopted for content-based approaches. In this sense, CBF and NLMSF are memory-based and model-based respectively.

  2. There are other factors that influence viewing time, such as hunger and length of stay in the museum (Sects. 1 and 4.1). However, for annotation purposes, using average visitor viewing time is a reasonable approach to take exhibit-area complexity into account.

  3. These thresholds, and those for the Populated model, were empirically determined.

  4. We did not annotate word senses manually, as this process is more cumbersome and more subjective than employing synsets. However, the incorporation of additional synsets is an interesting option that may be worth pursuing in the future, in particular in light of the promising results obtained with the Populated model (Sect. 5.2.2).

  5. It is not feasible to model situation- and user-specific factors, e.g., hunger. However, an interesting avenue for future research could involve refining our model to adjust viewing time on the basis of length of stay at the museum or viewing order of exhibit areas.

  6. The Bayesian information criterion (BIC), which is closely related to the Akaike information criterion (AIC) (Akaike 1974), is a criterion for selecting a model from a finite set of models. It does so by quantifying the ability of each parameterised model to predict the data, while penalising the complexity of the model (measured by the number of model parameters).

  7. Shrinkage to the mean is also applied for the collaborative viewing-time model in Sect. 4.3 and our second content-based model in Sect. 4.4, but is not required for our model based on Gaussian spatial processes in Sect. 4.5 due to the structure of the model.

  8. We also experimented with Spearman’s rank correlation coefficient, which performed similarly to Pearson’s correlation coefficient. We did not employ cosine similarity as done for CBF, because Pearson correlation tends to outperform cosine similarity in collaborative filtering (Breese et al. 1998).

  9. This functional specification of the correlation structure between exhibit-area pairs is crucial for the direct, interpretable application of parametric Gaussian (spatial) processes (with the ensuing low variance in parameter estimates), compared to more complex non-parametric Gaussian process approaches, e.g., (Schwaighofer et al. 2005).

  10. Slice Gibbs sampling is a Markov chain Monte Carlo algorithm for drawing random samples from a probability distribution. The method is based on the observation that one can sample a random variable by uniformly drawing from the region under the graph of its density function.

  11. This visitor-specific \(\log \)-viewing-time normalisation for calculating I2I differs from the normalisation required within our models based on normalised \(\log \) viewing time (Sects. 4.2 to 4.4), which is specific to each exhibit area (Eq. 1, Sect. 4.1). Although SPM may use I2I to measure the distance between exhibit areas, it does not otherwise require \(\log \)-viewing-time normalisations specific to an exhibit area or a visitor to generate viewing-time predictions. We also tested a variant of the I2I measure without visitor-specific normalisation. However, this variant yielded inferior results.

  12. Credible intervals (in Bayesian statistics) are analogous to confidence intervals in frequentist statistics.

    Table 7 SPM – Posterior mean parameter estimates for \(\tau ^2\), \(\phi \) and \(\nu \)
    Full size table

  13. Recall that model-based NLMSF outperforms memory-based CBF for the PV experiment, but CBF is slightly better than NLMSF for the IE experiment. As indicated in Sect. 5.2.2, we give more weight to the stronger results for the PV experiment, as the PV experiment evaluates the models’ performance with the progression of a visit, which is more realistic for a museum setting than the evaluation based on performance for individual exhibit areas.

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Acknowledgments

This research was supported in part by grant DP0770931 from the Australian Research Council, and a Postgraduate Publication Award from the Centre for Research in Intelligent Systems (CRIS) at Monash University. The authors thank Liz Sonenberg, Timothy Baldwin, Shlomo Berkovsky and Daniel F. Schmidt for their involvement at various stages of this research; Carolyn Meehan and her team from Museum Victoria for fruitful discussions and their support; David W. Albrecht for his help with statistical matters; David Abramson, Jeff Tan and Blair Bethwaite for their assistance with using the computer clusters; and Alfred Kobsa and the three anonymous reviewers for their thoughtful comments.

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  1. Faculty of Information Technology, Monash University, Clayton, VIC, 3800, Australia

    Fabian Bohnert & Ingrid Zukerman

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  1. Fabian Bohnert
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  2. Ingrid Zukerman
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Correspondence to Fabian Bohnert.

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This article integrates and extends research described in (Bohnert and Zukerman 2009a,b,c) and (Bohnert et al. 2009).

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Bohnert, F., Zukerman, I. Personalised viewing-time prediction in museums. User Model User-Adap Inter 24, 263–314 (2014). https://doi.org/10.1007/s11257-013-9141-8

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