@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}
}
@conference {786,
	title = {Policy Making Analysis and Practitioner User Experience},
	booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction},
	year = {2020},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>This article presents the work on social media analysis-driven policy-making platforms that are powered by classic social media analysis technologies, such as policy modelling, linguistic analysis, opinion mining, sentiment analysis and information visualization. The approach examines the user design perspective towards user experience in policymaking for all the innovative modules used. The technology behind such complex task is presented while the resulting platform is appraised on the potential for real world application. The findings drive the development and the requirements for the summative usability assessment tests. We also report on the level the practitioners adopted the policy formulation tools.</p>
},
	keywords = {Content Analysis, natural language interfaces, opinion mining, policy making, social network analysis, User Experience},
	doi = {10.1007/978-3-030-60114-0_29},
	author = {Dimitris Koryzis and Fotios Fitsilis and Dimitris Spiliotopoulos and Theocharis Theocharopoulos and Dionisis Margaris and Costas Vassilakis}
}
