@article {782,
	title = {Data-Assisted Persona Construction Using Social Media Data},
	journal = {Big Data and Cognitive Computing},
	volume = {4},
	year = {2020},
	month = {aug},
	pages = {21},
	abstract = {<p>User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers.</p>
},
	keywords = {Cultural Events, Persona, personalization, Topic Modelling, Usability, User Experience, User Interface Design},
	doi = {10.3390/bdcc4030021},
	author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis}
}
@article {723,
	title = {A Fine-grained Social Network Recommender System},
	journal = {Social Network Analysis and Mining},
	volume = {10},
	year = {2020},
	abstract = {Recommender systems have greatly evolved in recent years and have become an integral part of the Web. From e-commerce sites to mobile apps, our daily routine revolves around a series of {\textquotedblleft}small{\textquotedblright} decisions that are influenced by such recommendations. In a similar manner, online social networks recommend only a subset of the massive amount of content published by a user{\textquoteright}s friends. However, the prevalent approach for the content selection process in such systems is driven by the amount of interaction between the user and the friend who published the content. As a result, content of interest is often lost due to weak social ties. In this paper, we present a fine-grained recommender system for social ecosystems, designed to recommend media content (e.g., music videos, online clips) published by the user{\textquoteright}s friends. The system design was driven by the findings of our qualitative user study that explored the value and requirements of a recommendation component within a social network. The core idea behind the proposed approach was to leverage the abundance of preexisting information in each user{\textquoteright}s account for creating interest profiles, to calculate similarity scores at a fine-grained level for each friend. The intuition behind the proposed method was to find consistent ways to obtain information representations that can identify overlapping interests in very specific sub-categories (e.g., two users{\textquoteright} music preferences may only coincide on hard rock). While the system is intended as a component of the social networking service, we developed a proof-of-concept implementation for Facebook and explored the effectiveness of our underlying mechanisms for content analysis. Our experimental evaluation demonstrates the effectiveness of our approach, as the recommended content of interest was both overlooked by the existing Facebook engine and not contained in the users{\textquoteright} Facebook News Feed. We also conducted a user study for exploring the usability aspects of the prototype and found that it offers functionality that could significantly improve user experience in popular services.},
	keywords = {Content Analysis, Interest Profiles, Recommender Systems, Social Networks, Usability},
	issn = {1869-5469},
	doi = {10.1007/s13278-019-0621-7},
	author = {Markos Aivazoglou and Antonios Roussos and Dionisis Margaris and Costas Vassilakis and Sotiris Ioannidis and Jason Polakis and Dimitris Spiliotopoulos}
}
@article {781,
	title = {Multimodal Interaction: Correlates of Learners{\textquoteright} Metacognitive Skill Training Negotiation Experience},
	journal = {Information},
	volume = {11},
	year = {2020},
	month = {jul},
	pages = {381},
	abstract = {<p>Metacognitive training reflects knowledge, consideration and control over decision-making and task performance evident in any social and learning context. Interest in understanding the best account of effective (win-win) negotiation emerges in different social and cultural interactions worldwide. The research presented in this paper explores an extended study of metacognitive training system during negotiation using an embodied conversational agent. It elaborates on the findings from the usability evaluation employing 40 adult learners pre- and postinteraction with the system, reporting on the usability and metacognitive, individual- and community-level related attributes. Empirical evidence indicates (a) higher levels of self-efficacy, individual readiness to change and civic action after user-system experience, (b) significant and positive direct associations between self-efficacy, self-regulation, interpersonal and problem-solving skills, individual readiness to change, mastery goal orientation and civic action pre- and postinteraction and (c) gender differences in the perceptions of system usability performance according to country of origin. Theoretical and practical implications in tandem with future research avenues are discussed in light of embodied conversational agent metacognitive training in negotiation.</p>
},
	keywords = {big data, Cultural Events, Persona, personalization, Topic Modelling, Usability, User Experience, User Interface Design},
	doi = {10.3390/info11080381},
	author = {Dimitris Spiliotopoulos and Eleni Makri and Costas Vassilakis and Dionisis Margaris}
}
@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}
}
