@article {Margaris_2016,
	title = {Recommendation information diffusion in social networks considering user influence and semantics},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	month = {nov},
	pages = {108},
	publisher = {Springer Nature},
	abstract = {One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users{\textquoteright} profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items{\textquoteright} general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore online advertisement efficiency is raised.},
	keywords = {Collaborative filtering, Information diffusion, Quality of service, Semantic Information, Social Networks},
	doi = {10.1007/s13278-016-0416-z},
	url = {http://rdcu.be/mZ7e},
	author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis}
}
@conference {marg15a,
	title = {A Collaborative Filtering Algorithm with Clustering for Personalized Web Service Selection in Business Processes},
	booktitle = {Proceedings of RCIS 2015},
	year = {2015},
	month = {5},
	abstract = {Recommender systems aim to propose items that are expected to be of interest to the users. As one of the most successful approaches to building recommender systems, collaborative filtering exploits the known preferences of a group of users to formulate recommendations or predictions of the unknown preferences for other users. In many cases, collaborative filtering algorithms handle complex items, which are described using hierarchical tree structures containing rich semantic information. In order to make accurate recommendations on such items, the related algorithms must examine all aspects of the available semantic information. Thus, when collaborative filtering techniques are employed to adapt the execution of business processes, they must take into account the services{\textquoteright} Quality of Service parameters, so as to generate recommendations tailored to the individual user needs. In this paper, we present a collaborative filtering-based algorithm which takes into account the web services{\textquoteright} QoS parameters in order to tailor the execution of business processes to the preferences of users. An offline clustering technique is also introduced for supporting the efficient and scalable execution of proposed algorithm under the presence of large repositories of sparse data.},
	keywords = {business processes, clustering, collaborative filtering, hierarchical tree, performance, Quality of service, Web services},
	doi = {10.1109/RCIS.2015.7128877},
	author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis}
}
