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Mr. Markos Konstantakis
Aegean University

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0 Digital Culture
0 Evaluation Methodologies
0 Human Computer Interaction
0 Ubiquitous Computing
0 User Experience

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Journal article
Published: 04 June 2020 in Big Data and Cognitive Computing
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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.

ACS Style

Markos Konstantakis; Georgios Alexandridis; George Caridakis. A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies. Big Data and Cognitive Computing 2020, 4, 12 .

AMA Style

Markos Konstantakis, Georgios Alexandridis, George Caridakis. A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies. Big Data and Cognitive Computing. 2020; 4 (2):12.

Chicago/Turabian Style

Markos Konstantakis; Georgios Alexandridis; George Caridakis. 2020. "A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies." Big Data and Cognitive Computing 4, no. 2: 12.

Conference paper
Published: 01 November 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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The main feature of a Serious Game (SG) is its objective of supporting the player to achieve learning targets through a fun experience. The paper focuses on the creation of a digital cultural SG, named “The stolen painting”. The main goal of this game is to initiate users into art painting, through game activities that encourage the users to learn about some of the most famous paintings in the world and their creators. The theory of Gardner’s Multiple Intelligences (MI) is used for game profile identification through social media data mining techniques, or alternatively, through Multiple Intelligences Profiling Questionnaire (MIPQ) in order to reveal and quantify the different types of intellectual strengths (intelligences) that each user exhibits. Game’s progress is based on the three strongest intelligences of the player and the main objective of the player is to reveal the stolen painting’s identity.

ACS Style

Markos Konstantakis; Eirini Kalatha; George Caridakis. Cultural Heritage, Serious Games and User Personas Based on Gardner’s Theory of Multiple Intelligences: “The Stolen Painting” Game. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 490 -500.

AMA Style

Markos Konstantakis, Eirini Kalatha, George Caridakis. Cultural Heritage, Serious Games and User Personas Based on Gardner’s Theory of Multiple Intelligences: “The Stolen Painting” Game. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():490-500.

Chicago/Turabian Style

Markos Konstantakis; Eirini Kalatha; George Caridakis. 2019. "Cultural Heritage, Serious Games and User Personas Based on Gardner’s Theory of Multiple Intelligences: “The Stolen Painting” Game." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 490-500.