Next Article in Journal
The “Social” Side of Big Data: Teaching BD Analytics to Political Science Students
Previous Article in Journal
Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks
Previous Article in Special Issue
Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain
Open AccessArticle

A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies

Intelligent Interaction Research Group, Cultural Technology Department, University of the Aegean, 81100 Mytilene, Greece
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2020, 4(2), 12; https://doi.org/10.3390/bdcc4020012
Received: 25 April 2020 / Revised: 26 May 2020 / Accepted: 29 May 2020 / Published: 4 June 2020
(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)
Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach.
View Full-Text
Keywords: cultural tourism; typology; personalization; user experience; recommender systems; collaborative filtering; hybrid matrix factorization cultural tourism; typology; personalization; user experience; recommender systems; collaborative filtering; hybrid matrix factorization
Show Figures

Figure 1

MDPI and ACS Style

Konstantakis, M.; Alexandridis, G.; Caridakis, G. A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies. Big Data Cogn. Comput. 2020, 4, 12.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop