@inbook {808,
	title = {Identifying Reliable Recommenders in Users{\textquoteright} Collaborating Filtering and Social Neighbourhoods},
	booktitle = {Lecture Notes in Social Networks},
	year = {2021},
	pages = {51{\textendash}76},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>Recommender systems increasingly use information sourced from social networks to improve the quality of their recommendations. However, both recommender systems and social networks exhibit phenomena under which information for certain users or items is limited, such as the cold start and the grey sheep phenomena in collaborative filtering systems and the isolated users in social networks. In the context of a social network-aware collaborative filtering, where the collaborating filtering- and social network{\textendash}based neighbourhoods are of varying density and utility for recommendation formulation, the ability to identify the most reliable recommenders from each neighbourhood for each user and appropriately combine the information associated with them in the recommendation computation process can significantly improve the quality and accuracy of the recommendations offered. In this chapter, we report on our extensions on earlier works in this area which comprise of (1) the development of an algorithm for discovering the most reliable recommenders of a social network recommender system and (2) the development and evaluation of a new collaborative filtering algorithm that synthesizes the opinions of a user{\textquoteright}s identified recommenders to generate successful recommendations for the particular user. The proposed algorithm introduces significant gains in rating prediction accuracy (4.9\% on average, in terms of prediction MAE reduction and 4.2\% on average, in terms of prediction RMSE reduction) and outperforms other algorithms. The proposed algorithm, by design, utilizes only basic information from the collaborative filtering domain (user{\textendash}item ratings) and the social network domain (user relationships); therefore, it can be easily applied to any social network recommender system dataset.</p>
},
	keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Rating prediction, Recommender Systems, Social Networks},
	doi = {10.1007/978-3-030-67044-3_3},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos 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 {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}
}
@conference {Margaris_2019_asonam,
	title = {Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising},
	booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)},
	year = {2019},
	month = {aug},
	abstract = {Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users{\textquoteright} aspects are taken into consid-eration. However, social network information may be unavaila-ble since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited col-laborative filtering information, comprised only of users{\textquoteright} ratings on items, with limited social network information, com-prised only of users{\textquoteright} social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.},
	keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Online Advertising, Pearson Correlation Coefficient, Social Networks},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@conference {727,
	title = {Supporting small museums through social network semantic analysis (Greek title: Υποστήριξη μικρών μουσείων μέσω της σημασιολογικής ανάλυσης κοινωνικών δικτύων)},
	booktitle = {Proceedings of the 3nd Pan-Hellenic Conference on Digital Cultural Heritage-EuroMed 2019},
	year = {2019},
	abstract = {Greece has a significant historic, archaeological and cultural wealth, and a big number of museums and related venues. Some of them are big, both in size and resources -human as well as financial. Most venues however are small in size, are not connected with larger organizations, have constrained budgets and little or no permanent staff. In this paper, we focus on that particular category of museums. Many of these museums possess noteworthy collections, and it would be desirable if the general public could enjoy them. Unfortunately, in most cases, even the existence of the museums is not generally known, even to inhabitants of the cities at which the museums are located. The small number of visitors leads to confined revenues, and this in turn leads to a situation which is not financially viable. In order for a small museum to survive, its existence and value must be made known to the wide public. However, this is inhibited due to the lack of resources to finance advertising and the lack of specialized personnel. In this paper, we combine our experiences on small museums, which has been developed within the CrossCult H2020 project, with know-how on social network analysis which has been gained in the context of the PaloAnalytics national project, in order to offer viable solutions for promotion, which are tailored to the needs of small museums.},
	keywords = {semantic analysis, small-sized museums, Social Networks},
	author = {Manolis Wallace and Vassilis Poulopoulos and George Lepouras and Costas Vassilakis and Angeliki Antoniou}
}
@conference {724,
	title = {TripMentor: Challenges and prospects for culture and tourism in the Attica region (Greek title: {\textquotedblleft}TripMentor: προκλήσεις και προοπτικές για τον πολιτισμό και τον τουρισμό στην περιφέρεια Αττικής{\textquotedblleft})},
	booktitle = {Proceedings of the 3nd Pan-Hellenic Conference on Digital Cultural Heritage-EuroMed 2019},
	year = {2019},
	abstract = {This paper presents the TripMentor project which is related to the tourism and civilization in the region of Attica. The main goal of the project is the presentation, in two languages, of the rich cultural content of the region to the tourists, through an Internet-based environment and through mobile applications. The goal of the project is to guide the visitors through many possible alternative routes, which include points of interest, as well as enterprises with tourism-related offers, while at the same time attempts to combine these routes with cultural points of interest. The project interrelates information, venues and visitor profiles with an automated way, striving to offer a unique personalized experience, tailored to the needs and preferences of each user. The project challenges are mainly technical, since through the project procedures (a) information regarding the points of interest should be gathered in an automated fashion (b) information about events related to points of interest must be taken into account and (v) data related to users, and especially the user profiles must be discovered and exploited so that the available information can be synthesized into a unique experience. In this context, we present the project application domain and the related challenges.},
	keywords = {cultural information, culture, geospatial data, Social Networks, tourism, visitor profile},
	author = {Costas Vassilakis and Vassilis Poulopoulos and Manolis Wallace and Angeliki Antoniou and George Lepouras}
}
@article {margaris2018a,
	title = {Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems},
	journal = {Informatics},
	volume = {5},
	number = {2},
	year = {2018},
	month = {apr},
	pages = {21 pages},
	abstract = {One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1) we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2) we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user{\textquoteright}s interests, thus improving prediction quality.},
	keywords = {collaborative filtering, concept drift, Evaluation, Recommender Systems, shift of interest, Social Networks},
	doi = {10.3390/informatics5020021},
	url = {http://www.mdpi.com/2227-9709/5/2/21},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-tldks,
	title = {Enhancing User Rating Database Consistency Through Pruning},
	journal = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV},
	volume = {LNCS, volume 10620},
	number = {Special issue on Consistency and Inconsistency in Data-centric Applications},
	year = {2017},
	pages = {33{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	abstract = {Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by the interests and likings of people may change: people listen to different singers or even different types of music, watch different types of movies, read different types of books and so on. Due to this type of changes, an amount of inconsistency is introduced in the database since a portion of it does not reflect the current preferences of the user, which is its intended purpose. In this paper, we present a pruning technique that removes old aged user behavior data from the ratings database, which are bound to correspond to invalidated preferences of the user. Through pruning (1) inconsistencies are removed and data quality is upgraded, (2) better rating prediction generation times are achieved and (3) the ratings database size is reduced. We also propose an algorithm for determining the amount of pruning that should be performed, allowing the tuning and operation of the pruning algorithm in an unsupervised fashion. The proposed technique is evaluated and compared against seven aging algorithms, which reduce the importance of aged ratings, and a state-of-the-art pruning algorithm, using datasets with varying characteristics. It is also validated using two distinct rating prediction computation strategies, namely collaborative filtering and matrix factorization. The proposed technique needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values), can be used in all rating databases that include a timestamp and has been proved to be effective in any size of users-items database and under two rating prediction computation strategies. Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author{\textquoteright}s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.},
	keywords = {Collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/978-3-662-55947-5_3},
	url = {https://link.springer.com/article/10.1007\%2Fs11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-soca-17,
	title = {Exploiting Internet of Things information to enhance venues{\textquoteright} recommendation accuracy},
	journal = {Service Oriented Computing and Applications},
	volume = {todb},
	year = {2017},
	month = {aug},
	pages = {393{\textendash}409},
	publisher = {Springer Nature},
	abstract = {In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theatres, etc.), such as the user{\textquoteright}s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.},
	keywords = {collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/s11761-017-0216-y},
	url = {https://link.springer.com/article/10.1007/s11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@inbook {marg16d,
	title = {Knowledge-Based Leisure Time Recommendations in Social Networks},
	booktitle = {Current Trends on Knowledge-Based Systems},
	series = {Intelligent Systems Reference Library},
	year = {2017},
	pages = {23{\textendash}48},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	chapter = {Knowledge-Based Leisure Time Recommendations in Social Networks},
	abstract = {We introduce a novel knowledge-based recommendation algorithm for leisure time information to be used in social networks, which enhances the state-of-the-art in this algorithm category by taking into account (a) qualitative aspects of the recommended places (restaurants, museums, tourist attractions etc.), such as price, service and atmosphere, (b) influencing factors between social network users, (c) the semantic and geographical distance between locations and (d) the semantic categorization of the places to be recommended. The combination of these features leads to more accurate and better user-targeted leisure time recommendations.},
	keywords = {Attribute constraints, collaborative filtering, Knowledge-based Recommender Systems, Semantic Information, Social Networks},
	doi = {10.1007/978-3-319-51905-0_2},
	author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis},
	editor = {Giner Alor Hernandez and Rafael Valencia Garcia}
}
@article {vassilakis-snam-2017a,
	title = {Stimulation of reflection and discussion in museum visits through the use of social media},
	journal = {Social Network Analysis and Mining},
	volume = {7},
	number = {40},
	year = {2017},
	month = {12},
	pages = {1-12},
	publisher = {Springer-Verlag GmbH Austria},
	abstract = {In this paper, we examine how social media can be linked to cultural heritage and in particular how we can incorporate games, social networks, history reflection and culture. More specifically, we explore the following aspects: (a) how social media sites can be integrated into the museum user experience (b) how user interactions within the social media, both within the context of the museum experience and outside it, can be exploited to enhance the quality of recommendations made to the users, (c) how trending topics from social media can be used to link museum exhibits with today{\textquoteright}s topics of interest and (d) how multi-level related terms extraction from social media data can lead to proposals for reflections to users. The end goal is to provide increased stimuli for users to study exhibits deeper and reflect on them, as well as to trigger discussion between the users, thus maximizing the impact of a museum visit.},
	keywords = {big data, cognitive style, Communication, reflection, Social Networks, user profiling},
	doi = {10.1007/s13278-017-0460-3},
	url = {https://link.springer.com/article/10.1007/s13278-017-0460-3},
	author = {Costas Vassilakis and Angela Antoniou and Giorgos Lepouras and Vassilis Poulopoulos and Manolis Wallace and Stavroula Bampatzia and Ioannis Bourlakos}
}
@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}
}
