Abstract
Today’s design of e-services for tourists means dealing with a big quantity of information and metadata that designers should be able to leverage to generate perceived values for users. In this paper we revise the design choices followed to implement a recommender system, highlighting the data processing and architectural point of view, and finally we propose a multi-agent recommender system.
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Notes
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This represents the distance between two activities, not the similarity, but we can still easily get, for each activity, the most similar ones by sorting according to the distance, ascending.
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Acknowledgements
This work was partly supported by the “eTravel project” funded by the “Provincia di Trento”, and by the program “Piano sostegno alla ricerca 2018” funded by Università degli Studi di Milano.
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Bellandi, V., Ceravolo, P., Tacchini, E. (2019). Modeling a Multi-agent Tourism Recommender System. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C., Meersman, R. (eds) On the Move to Meaningful Internet Systems: OTM 2019 Conferences. OTM 2019. Lecture Notes in Computer Science(), vol 11877. Springer, Cham. https://doi.org/10.1007/978-3-030-33246-4_46
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