@article {vassilakis-snam-2017a,
	title = {Stimulation of reflection and discussion in museum visits through the use of social media},
	journal = {Social Network Analysis and Mining},
	volume = {7},
	number = {40},
	year = {2017},
	month = {12},
	pages = {1-12},
	publisher = {Springer-Verlag GmbH Austria},
	abstract = {In this paper, we examine how social media can be linked to cultural heritage and in particular how we can incorporate games, social networks, history reflection and culture. More specifically, we explore the following aspects: (a) how social media sites can be integrated into the museum user experience (b) how user interactions within the social media, both within the context of the museum experience and outside it, can be exploited to enhance the quality of recommendations made to the users, (c) how trending topics from social media can be used to link museum exhibits with today{\textquoteright}s topics of interest and (d) how multi-level related terms extraction from social media data can lead to proposals for reflections to users. The end goal is to provide increased stimuli for users to study exhibits deeper and reflect on them, as well as to trigger discussion between the users, thus maximizing the impact of a museum visit.},
	keywords = {big data, cognitive style, Communication, reflection, Social Networks, user profiling},
	doi = {10.1007/s13278-017-0460-3},
	url = {https://link.springer.com/article/10.1007/s13278-017-0460-3},
	author = {Costas Vassilakis and Angela Antoniou and Giorgos Lepouras and Vassilis Poulopoulos and Manolis Wallace and Stavroula Bampatzia and Ioannis Bourlakos}
}
@conference {Golemati200662,
	title = {A context-based adaptive visualization environment},
	booktitle = {Proceedings of the International Conference on Information Visualisation},
	year = {2006},
	note = {cited By (since 1996) 0},
	month = {7},
	pages = {62-67},
	abstract = {Digital libraries and historical archives are increasingly employing visualization systems to facilitate the information retrieval and knowledge extraction tasks of their users. Typically, each organization employs a single visualization system, which may not suit best the needs of certain user groups, specific tasks, or properties of document collections to be visualized. In this paper we present a context-based adaptive visualization environment, which embeds a set of visualization methods into a visualization library, from which the most appropriate one is selected for presenting information to the user. Methods are selected by examining parameters related to the user profile, system configuration and the set of data to be visualized, and employing a set of rules to assess the suitability of each method. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria. Β{\textcopyright} 2006 IEEE.},
	keywords = {(PL) properties, Adaptive systems, Adaptive visualization, Behavioral research, Digital libraries, document collections, Extraction, Information analysis, Information science, Information services, Information systems, Information visualization, Knowledge extraction, Libraries, Retrieval (MIR), Search engines, Selection criterion, Set of rules, Solvent extraction, Specific tasks, System configurations, User behaviors, User groups, user profiling, Visualization, visualization methods, Visualization systems},
	doi = {10.1109/IV.2006.5},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-45149121827\&partnerID=40\&md5=ec45ad555b132faaf0bd2ca53e3292dc},
	author = {M. Golemati and C. Halatsis and Costas Vassilakis and Akrivi Katifori and Giorgos Lepouras}
}
