@article {820,
	title = {Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets},
	journal = {Big Data and Cognitive Computing},
	volume = {7},
	year = {2023},
	month = {mar},
	pages = {59},
	abstract = {<p>Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered {\textquotedblleft}close{\textquotedblright} to the user for whom the prediction is being generated have given to the same product or service. In general, {\textquotedblleft}close{\textquotedblright} users for some user u correspond to users that have rated items similarly to u and these users are termed as {\textquotedblleft}near neighbors{\textquotedblright}. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset{\textquoteright}s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method{\textquoteright}s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.</p>
},
	keywords = {collaborative filtering, Evaluation, low density datasets, personalization, rating prediction quality, Recommender Systems, reliability},
	doi = {10.3390/bdcc7020059},
	url = {https://www.mdpi.com/2504-2289/7/2/59},
	author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos and Stefanos Ougiaroglou}
}
@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 {margaris15c,
	title = {On Replacement Service Selection in WS-BPEL Scenario Adaptation},
	booktitle = {Proceedings of the 8th IEEE International Conference on Service Oriented Computing \& Applications (SOCA 2015)},
	year = {2015},
	month = {9},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {WS-BPEL scenario execution adaptation has been proposed by numerous researchers as a response to the need of users to tailor the WS-BPEL scenario execution to their individual preferences; these preferences are typically expressed through Quality of Service (QoS) policies, which the adaptation mechanism considers in order to select the services that will ultimately be invoked to realize the desired business process. In this paper, we consider a number of issues related to WS-BPEL scenario adaptation, aiming to enhance adaptation quality and improve the QoS offered to end users. More specifically, with the goal of broadening the service selection pool we (a) discuss the identification of potential services that can be used to realize a functionality used in the WS-BPEL scenario and (b) elaborate on transactional semantics that invocations to multiple services offered by the same provider may bear. We also describe and validate an architecture for realizing the proposed enhancements.},
	doi = {10.1109/SOCA.2015.11},
	author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis}
}
