@article {749,
	title = {Human behaviour in multimodal interaction: main effects of civic action and interpersonal and problem-solving skills},
	journal = {Journal of Ambient Intelligence and Humanized Computing},
	volume = {TBD},
	year = {2020},
	month = {mar},
	pages = {TBD},
	abstract = {Metacognitive skill training may rest within any kind of social interaction that requires awareness of what an individual and others think, in social, educational and organizational settings alike. This work explores an extensive study of multimodal application interaction (virtual agent, spoken dialogue, visual communication of progress) for metacognitive skill training via negotiation skill training settings. Human behaviour, as effected by civic action and interpersonal and problem-solving skill training, is investigated through interaction sessions with a virtual agent on multimodal multiparty negotiation. This work reports on the results of the user-system evaluation sessions involving 41 participants before-and-after interaction with the system, integrating macro- (dialogue system performance) and micro- (metacognitive-related and individual-and-community level-related attitudes and skills) factors. Findings indicate significant and positive relationships between user and system evaluation questions after interaction with the dialogue system and between self-efficacy, self-regulation, individual readiness to change, mastery goal orientation, interpersonal and problem-solving skills and civic action before-and-after the interaction experience. Implications, limitations and further research issues are discussed in light of context of the multimodal interaction and its effects on the human behaviour during metacognitive skill training.},
	keywords = {Human Behaviour, Individual- and Community-Level Related Attitudes and Skills, Metacognitive, Multimodal Interaction, Skill Training},
	doi = {10.1007/s12652-020-01846-x},
	author = {Markri, Eleni and Spiliotopoulos, Dimitris and Vassilakis, Costas and Margaris, Dionisis}
}
@article {722,
	title = {Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks},
	journal = {Social Network Analysis and Mining},
	volume = {9},
	year = {2019},
	month = {Oct},
	pages = {64},
	abstract = {One of the major problems that social media front is to continuously produce successful, user-targeted information, in the form of recommendations, which are produced by applying methods from the area of recommender systems. One of the most important applications of recommender systems in social networks is venue recommendation, targeted by the majority of the leading social networks (Facebook, TripAdvisor, OpenTable, etc.). However, recommender systems{\textquoteright} algorithms rely only on the existence of numeric ratings which are typically entered by users, and in the context of social networks, this information is scarce, since many social networks allow only reviews, rather than explicit ratings. Even if explicit ratings are supported, users may still resort to expressing their views and rating their experiences through submitting posts, which is the predominant user practice in social networks, rather than entering explicit ratings. User posts contain textual information, which can be exploited to compute derived ratings, and these derived ratings can be used in the recommendation process in the lack of explicitly entered ratings. Emerging recommender systems encompass this approach, without however tackling the fact that the ratings computed on the basis of textual information may be inaccurate, due to the very nature of the computation process. In this paper, we present an approach which extracts features of the textual information, a widely available source of information in venue category, to compute a confidence metric for the ratings that are computed from texts; then, this confidence metric is used in the user similarity computation and venue rating prediction formulation process, along with the computed rating. Furthermore, we propose a venue recommendation method that considers the generated venue rating predictions, along with venue QoS, similarity and spatial distance metrics in order to generate venue recommendations for social network users. Finally, we validate the accuracy of the rating prediction method and the user satisfaction from the recommendations generated by the recommendation formulation algorithm. Conclusively, the introduction of the confidence level significantly improves rating prediction accuracy, leverages the ability to generate personalized recommendations for users and increases user satisfaction.},
	keywords = {collaborative filtering, Confidence level, Rating prediction, Recommender Systems, Social Networks, Textual information, Uncertainty, Venue recommendation formulation},
	issn = {1869-5469},
	doi = {10.1007/s13278-019-0610-x},
	url = {https://doi.org/10.1007/s13278-019-0610-x},
	author = {Margaris, Dionisis and Vassilakis, Costas and Spiliotopoulos, Dimitris}
}
