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Recommendation in museums: paths, sequences, and group satisfaction maximization

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Abstract

This work addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum. Differently from a recommender system for an e-commerce application, the problem, here, is trying to maximize the satisfaction of the proposed recommendations, while taking into account an items’ ordering that satisfies each group member during the sequence and the artworks locations in the museum. Moreover, since many visitors may not be able to visit every artwork, the recommender system should provide suggestions while satisfying temporal visit constraints. The problem formulation is discussed together with the characteristics of a feasible solution. An exact search algorithm from the literature is used to efficiently solve the problem and to define the prerequisites for the recommender system. Finally, we evaluate a prototype implementation with both an offline analysis and a pilot study in a simulated museum environment.

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Notes

  1. A topological sort is a linear ordering of all nodes of a DAG such that each node comes before all nodes connected to its outgoing edges.

  2. http://grouplens.org/datasets/movielens/

  3. https://mahout.apache.org

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Authors and Affiliations

  1. Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Napoli, Italy

    Silvia Rossi, Clemente Galdi & Luca Russo

  2. Dipartimento di Matematica e Applicazioni, Università degli Studi di Napoli Federico II, Napoli, Italy

    Francesco Barile

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  1. Silvia Rossi
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  2. Francesco Barile
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  3. Clemente Galdi
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  4. Luca Russo
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Correspondence to Silvia Rossi.

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Preliminary version appeared as [25]

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Rossi, S., Barile, F., Galdi, C. et al. Recommendation in museums: paths, sequences, and group satisfaction maximization. Multimed Tools Appl 76, 26031–26055 (2017). https://doi.org/10.1007/s11042-017-4869-5

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  • DOI: https://doi.org/10.1007/s11042-017-4869-5

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