@inbook {807,
	title = {Safe Travelling Period Recommendation to High Attack Risk European Destinations Based on Past Attack Information},
	booktitle = {Lecture Notes in Social Networks},
	year = {2021},
	pages = {77{\textendash}100},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>Terrorism is a significant deterrent for tourism. It affects both visitors and local citizens and personnel of a country or area. On one hand, the potential visitor will probably avoid travelling to a high attack risk country, due to safety reasons, hence will miss the opportunity to visit it, and, on the other hand, the country{\textquoteright}s tourism will decline. This work addresses the aforementioned problem by (1) showing that relatively safe visiting periods for high attack risk European countries can be predicted with high accuracy, using limited information, comprising of attack and fatality data from the past years, which are widely available, and (2) developing an algorithm that recommends relatively safe periods to potential travellers. The results of this work will be useful for tourists, visitors, businesses and operators, as well as relevant stakeholders and actors.</p>
},
	keywords = {Evaluation, Recommendation algorithm, Risk Calculation, Safety Perception, Safety prediction, Terrorist Attacks, tourism},
	doi = {10.1007/978-3-030-67044-3_3},
	author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis}
}
@article {770,
	title = {A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems},
	journal = {Big Data and Cognitive Computing},
	volume = {4},
	year = {2020},
	month = {4/2020},
	pages = {6},
	abstract = {This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences.},
	keywords = {cultural experience, intelligent interaction, Mixed Reality, semantics, Usability},
	doi = {https://doi.org/10.3390/bdcc4020006},
	author = {Costas Vassilakis and Konstantinos Kotis and Dimitris Spiliotopoulos and Dionisis Margaris and Vlasios Kasapakis and Christos-Nikolaos Anagnostopoulos and Georgios Santipantakis and George A. Vouros and Theodore Kotsilieris and Volha Petukhova and Andrei Malchanau and Ioanna Lykourentzou and Kaj Michael Helin and Artem Revenko and Nenad Gligoric and Boris Pokric}
}
@conference {777,
	title = {Semantics-Driven Conversational Interfaces for Museum Chatbots},
	booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction, volume "Culture and Computing"},
	year = {2020},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>This work addresses the challenges of creating usable and personalized conversational interfaces for broad, yet applicable, domains that require user engagement and learning, such as museum chatbots. Whether the chatbots are standalone or coupled with virtual agents or real-life robots, the functional requirements for interaction that targets specific learning aspects would be expected to be more or less similar. This work reports on experimental semantics-driven conversational interface design for chatbots in museum settings, targeting visitors to converse about exhibits and learn information about their style, the artists, the era, and other aspects related to them. Depending on the semantics (presentation, learning, exploration), chatbot scenarios were designed and evaluated by participants in a formative evaluation. The evaluation show that user requirement perception manifests in expectations on the semantic level, instead of just the technical level. The results between the scenarios are compared to see how the semantics considered for the design transferred to the implementation and to the user perception.</p>
},
	keywords = {chatbots, conversational interfaces, cultural technology, semantics},
	doi = {10.1007/978-3-030-50267-6_20},
	author = {Dimitris Spiliotopoulos and Konstantinos Kotis and Costas Vassilakis and Dionisis Margaris}
}
@conference {Margaris_2019_asonam,
	title = {Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising},
	booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)},
	year = {2019},
	month = {aug},
	abstract = {Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users{\textquoteright} aspects are taken into consid-eration. However, social network information may be unavaila-ble since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited col-laborative filtering information, comprised only of users{\textquoteright} ratings on items, with limited social network information, com-prised only of users{\textquoteright} social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.},
	keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Online Advertising, Pearson Correlation Coefficient, Social Networks},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
